Project description

Uncovering immune evasion mechanisms of leukemic cells from natural killer cells. In this sub-project RNA sequencing of leukemic cells is used to discover transcriptional changes resulting in NK cell-resistance. There is another sub-project using ATAC-seq (transposase-accessible chromatin) to epigenetic changes resulting in NK cell-resistance - this is not part of this report.

Experimental design

Taken from discussion summary (‘21009_Porject discussion summary.docx’):

  • 4 different cell lines (p185#13G, p185#13H, p185#15M, p185#15O). ADD description about cell line differences.
  • All conditions in technical triplicates and 2 biological replicates (biological replicates can be added any time, if there is a need).
  • Technical replicate means that we already start the co-culture in a separate well.

Biological and technical replicates

Timeline of the experiment

  • On time point Day 4 we FACS sort the original tumor cells (like Day 0) and the tumor cells which were co-cultured for 4 days with NK cells for the following analyses: ATAC seq, RNA seq and barcode analysis. Additionally we co-culture a certain amount of the same tumor cells again with NK cells for the analysis of Day 10.
  • On time point Day 10 we FACS sort the tumor cells which were co-cultured for 10 days with NK cells for the same analyses: ATAC seq, RNA seq and barcode analysis.

Experiment Timepoints

RNA-seq data analysis

RNA-seq methods: QuantSeq 3’ mRNA-Seq - 1x50bp HiSeq 3000/4000.

Pre-processing: ADD methods+versions for preprocessing Pre-processing pipeline

Downstream analysis: ADD methods+versions for downstream!

MultiQC report ADD full multiqc report description. Few observations:

# importing only key functions that are actually used - not to polute namespace!
import::from(readr, read_csv)
import::from(magrittr, "%>%")
import::from(dplyr, mutate, select, filter, rename, arrange, desc, group_by, summarise, ungroup)  # dplyr_mutate = mutate
import::from(purrr, map)
import::from(future, plan, multisession, sequential)
import::from(furrr, furrr_options, future_map2)
import::from(ggplot2, .all=TRUE) # importing all as there is too many
import::from(grid, gpar) # needed in complexheatmap
import::from(kableExtra, kable_styling, kbl)
import::from(.from = SummarizedExperiment, colData, assay) # used in every .Rmd
import::from(.from = tableone, CreateTableOne)

Preparing datasets

# importing only key functions that are actually used - not to polute namespace!
import::from(.from = readr, read_csv, cols)
import::from(magrittr, "%>%")
import::from(dplyr, mutate, select, filter, rename, arrange, desc, group_by, summarise, ungroup)  # dplyr_mutate = mutate
import::from(purrr, map)
import::from(future, plan, multisession, sequential)
import::from(furrr, furrr_options, future_map2)
import::from(ggplot2, .all=TRUE) # importing all as there is too many
import::from(grid, gpar) # needed in complexheatmap
import::from(kableExtra, kable_styling, kbl)

import::from(.from = GenomicFeatures, makeTxDbFromGFF)
import::from(.from = AnnotationDbi, annot_db_keys = keys, annot_db_select = select)
import::from(.from = DESeq2, .all=TRUE)
import::from(.from = tximport, tximport)

import::from(.from = here::here("utils/filterDatasets.R"), "filterDatasets", .character_only=TRUE) # used for filtering
import::from(.from = here::here("utils/generateEnsemblAnnotation.R"), "generateEnsemblAnnotation", .character_only=TRUE) # used for filtering
experiment_name="nk_tum_immunoedit_complete" # this can be a subset analysis - e.g. just batch1,...
experiment_design="1"  # used only to construct initial dds object
abs_filt_samples=3

# parameters for annotation
biomart_host = "http://nov2020.archive.ensembl.org"
#biomart_host = "https://www.ensembl.org"
biomart_Ens_version = "Ensembl Genes 102"
biomart_dataset="mmusculus_gene_ensembl"

# directories
output_dir = paste0("results_dir", "/",experiment_name,"/")
dir.create(output_dir)

Preparing metadata and expression data and combining into a dds (DESeqDataSet object) that is ready for analyses. dds object is further pre-filtered to remove lowly expressed genes (FPM (fragments per million mapped fragments) > 1 in at least 3 samples). Salmon quantification (quant.sf) transcript abundance estimates (transcript expression) is imported and converted to gene expression using tximport.

# Preparing metadata
rnaseq_metadata_file <- here::here(config$metadata$rnaseq$rnaseq_metadata)
rnaseq_metadata_df_raw <- readr::read_csv(file = rnaseq_metadata_file)  # 133 samples; 42 samples from other project?
quantseq_files <- here::here(config$metadata$rnaseq$rnaseq_salmon_files)

# salmon quant files
quant_files_md5sums_raw <- readr::read_table(file = quantseq_files, col_names = c("md5sum", "file_path"))
quant_files_md5sums <- quant_files_md5sums_raw %>%
  dplyr::mutate(absolute_quant_files_path = gsub(pattern = "\\./", replacement = paste0(dirname(quantseq_files), "/"), file_path),
                sample_name = gsub(pattern = "(\\./)(S_.+)(_0_.+)", replacement = "\\2", file_path)) %>%
  # for batch3
  dplyr::mutate(sample_name = gsub(pattern = "(\\./)(R.+)(_S.+)", replacement = "\\2", sample_name)) %>%
  # EK were samples from other experiment - they were removed from quant.sf list, but in case remove them here as well!
  dplyr::filter(!grepl(pattern = "\\./EK.+", x = sample_name)) %>%
  dplyr::mutate(bsf_sample_name = gsub(pattern = "(\\./)(S_.+)(_quant/quant.sf)", replacement = "\\2", x = file_path)) %>%
  # for batch 3
  dplyr::mutate(bsf_sample_name = gsub(pattern = "(\\./)(R.+_S.+)(_quant/quant.sf)", replacement = "\\2", x = bsf_sample_name))

# experiment info - match experiment to run
experiment_info_batch1_2_raw <- readr::read_csv("datasets/metadata/rnaseq_merge_sample_sheet.csv", col_types =cols(.default = "c"))
experiment_info_batch3_raw <- readr::read_csv("datasets/metadata/rnaseq_sample_sheet_batch3.csv", col_types = cols(.default = "c"))

experiment_info_raw <- dplyr::bind_rows(experiment_info_batch1_2_raw, experiment_info_batch3_raw)

# EK samples are from different experiment
experiment_info <- experiment_info_raw %>%
  dplyr::filter(!grepl(pattern = "EK.+", x = original_sample_name)) %>%
  dplyr::rename(library_name = experiment,
                bsf_sample_name = sample_name) %>%
  # fixing library_name for experiment EXP9.8
  dplyr::mutate(library_name = dplyr::if_else(library_name == "BSF_1241", "R0128_L5464", library_name)) %>%
  dplyr::mutate(sample_name_unique = dplyr::case_when(grepl(pattern = "^R", x=original_sample_name) ~ paste0(original_sample_name, "_", library_name),
                                                      TRUE ~ paste0("S_", original_sample_name, "_", library_name)))
# loading metadata and adding experiment and salmon data
rnaseq_metadata_df <- rnaseq_metadata_df_raw %>%
  dplyr::mutate(condition_complete = condition) %>%
  dplyr::mutate(condition = gsub(pattern = "(.+)(_Day.+)", replacement = "\\1", condition_complete)) %>%
  dplyr::mutate(condition = gsub(pattern = "\\+", replacement = "_plus_", condition),
                timepoint_cell_harvesting = gsub(pattern = " ", replacement = "_", timepoint_cell_harvesting),
                experiment = gsub(pattern = " ", replacement = "", original_experiment)) %>%
  dplyr::mutate(sample_name_unique = dplyr::case_when(grepl(pattern = "^R", x=sample_name) ~ paste0(sample_name, "_", library_name),
                   TRUE ~ paste0("S_", sample_name, "_", library_name)))

quant_files_md5sums_annot <- quant_files_md5sums %>%
  dplyr::left_join(., experiment_info, by = "bsf_sample_name") %>%
  dplyr::select(-sample_name, -library_name)

rnaseq_metadata_df_annot <- rnaseq_metadata_df %>%
  dplyr::left_join(., quant_files_md5sums_annot, by="sample_name_unique") %>%
  dplyr::mutate(original_sample_name = sample_name) %>%
  dplyr::mutate(sample_name = sample_name_unique) %>%
  dplyr::mutate(filenames = sample_name_unique)

#table(rnaseq_metadata_df_annot$original_experiment)
#table(rnaseq_metadata_df_annot$cell_line_label)
rnaseq_metadata_df_annot_reduced <- rnaseq_metadata_df_annot %>%
  #dplyr::filter(original_experiment == "EXP9_6") %>%
  #dplyr::filter(condition %in% c("Tumor_only", "Tumor_plus_NK_Day14")) %>%
  dplyr::mutate(technical_replicate = factor(technical_replicate, levels = c("1", "2", "3")),
                condition = factor(condition, levels = c("Tumor_only", "Tumor_plus_NK", "Tumor_plus_IFNg", "Tumor_plus_WT_NK", "Tumor_plus_PrfKO_NK")),
                timepoint_cell_harvesting = factor(timepoint_cell_harvesting, levels = c("timepoint_0", "timepoint_1", "timepoint_2")),
                cell_line_label = as.factor(cell_line_label),
                experiment = factor(experiment, levels = c("EXP9.4", "EXP9.5", "EXP9.6", "EXP9.7", "EXP9.8")),
                condition_tp = paste0(condition,"_",timepoint_cell_harvesting)) %>%
  dplyr::mutate(condition_tp = factor(condition_tp, levels = c("Tumor_only_timepoint_0", 
                                                               "Tumor_only_timepoint_1", "Tumor_only_timepoint_2",
                                                               "Tumor_plus_NK_timepoint_1", "Tumor_plus_NK_timepoint_2",
                                                               "Tumor_plus_WT_NK_timepoint_2",
                                                               "Tumor_plus_IFNg_timepoint_2", "Tumor_plus_PrfKO_NK_timepoint_2"))) %>%
  #fixing day_cell_harvesting 
  dplyr::mutate(day_cell_harvesting = gsub(pattern = " ", replacement = "", day_cell_harvesting)) %>%
  dplyr::mutate(day_cell_harvesting = factor(day_cell_harvesting, levels=c("Day0", "Day4", "Day6", "Day10", "Day14", "Day16")))

metadata <- rnaseq_metadata_df_annot_reduced #cond_data
#rm(reseq_metadata)
rownames(metadata) <- metadata$filenames

# use alternative sample names for multiQC
# renaming for the multiQC report
# metadata_multiqc <- metadata %>%
#   dplyr::select(bsf_sample_name, experiment, technical_replicate, cell_line_label, condition, timepoint_cell_harvesting) %>%
#   dplyr::mutate(cell_line_label = paste0("cl", cell_line_label), 
#                 condition = gsub(pattern = "Tumor", replacement = "T", x = condition),
#                 timepoint_cell_harvesting = gsub(pattern = "timepoint", replacement = "tp", x=timepoint_cell_harvesting),
#                 technical_replicate = paste0("repl",technical_replicate)) %>%
#   dplyr::mutate(alt_name = paste(experiment, cell_line_label, technical_replicate, condition, timepoint_cell_harvesting, sep="-")) %>%
#   dplyr::select(bsf_sample_name, alt_name)
# 
# readr::write_tsv(metadata_multiqc, file = paste0(OUTPUT_DIR, experiment_name, "_metadata_multiqc.tsv"))
#TODO:
# [ ] - add md5sums for tx2gene_file
gtf_file <- file.path(annotation_dir, "Mus_musculus.GRCm38.102.gtf") # needed only once to generate tx2gene.tsv
tx2gene_file <- file.path(annotation_dir, "EnsDb.Mmusculus.v102_tx2gene.tsv")
tx2gene_file_md5sum <- file.path(annotation_dir, "EnsDb.Mmusculus.v102_tx2gene.md5sum")

if (!file.exists(tx2gene_file)) {
  message(tx2gene_file, " does not exist: generating!")
  txDB <- GenomicFeatures::makeTxDbFromGFF(gtf_file, format="gtf")
  
  # AnnotationDbi::keys
  tx_name <- annot_db_keys(txDB, keytype="TXNAME")
  
  # AnnotationDbi::select
  tx2gene_gtf <- annot_db_select(txDB,
                                 keys = tx_name,
                                 columns="GENEID",
                                 keytype = "TXNAME")
  
  
  # saving tx2gene into destdir
  write.table(tx2gene_gtf,
              file = tx2gene_file,
              sep = "\t",
              col.names = FALSE,
              row.names = FALSE,
              quote = FALSE)
  
  tx2gene_md5sum <- digest::digest(tx2gene_file, file=TRUE, algo="md5")
  write.table(paste(tx2gene_md5sum, basename(tx2gene_file), collapse = "\t"),
              file = tx2gene_file_md5sum,
              sep = "\t",
              col.names = FALSE,
              row.names = FALSE,
              quote = FALSE)
  
} else {
  message(tx2gene_file, " exists: loading!")
  tx2gene <- readr::read_tsv(tx2gene_file,
                             col_names = c("tx_id", "gene_id"),
                             show_col_types = FALSE)
}
precomputed_objects_filename <- paste0(experiment_name, "_dds_objects.RData")
precomputed_objects_file <- file.path(output_dir, precomputed_objects_filename)

precomputed_transf_objects_filename <- paste0(experiment_name, "_log2_vsd_filt_objects.RData")
precomputed_transf_objects_file <- file.path(output_dir, precomputed_transf_objects_filename)

if(!file.exists(precomputed_objects_file)){
  cat("RNA objects file '", precomputed_objects_filename, "' does not exist in the OUTPUT_DIR...\n")
  cat("generating", precomputed_objects_filename, "\n")
  # Generating dds and filtered dds objects ----
  #require(DESeq2)
  # here there is a single sample so we use ~1.
  # expect a warning that there is only a single sample...
  
  # generating dds object ----
  # txi_object <- generateTxi(metadata_object=metadata,
  #                           column_names=c("absolute_quant_files_path", "filenames"),
  #                           tx2gene = tx2gene)
  
  quant_files <- metadata$absolute_quant_files_path
  names(quant_files) <- metadata$sample_name
  
  txi_object <- tximport::tximport(quant_files,
                         type="salmon",
                         tx2gene = tx2gene,
                         ignoreTxVersion = TRUE)
  
  sum(rownames(metadata) == colnames(txi_object$counts)) # check if names match between metadata and counts data
  cat("... txi_object\n")
  
  dds <- DESeq2::DESeqDataSetFromTximport(txi_object, 
                                          colData = metadata, 
                                          design = as.formula(paste0("~", experiment_design)))
  
  cat("... dds\n")
  dds <- DESeq2::estimateSizeFactors(dds)
  dds <- DESeq2::DESeq(dds, minReplicatesForReplace=Inf) # do not replace outliers based on replicates
  
  # filtering dds
  cat("... dds filtering\n")
  dds_filt <- filterDatasets(dds, abs_filt = TRUE, 
                             abs_filt_samples = abs_filt_samples) # at least in N samples, which is a smallest group size
  dds_filt <- DESeq2::estimateSizeFactors(dds_filt)

  # Generate annotation file using biomaRt
  #FIXME:
  # Issues with Curl when using https:// and http:// throws a warning:
  #  Warning: Ensembl will soon enforce the use of https.
  #   Ensure the 'host' argument includes "https://"
  cat("... fetching annotation from biomart\n")
  ensemblAnnot <- generateEnsemblAnnotation(ensembl_ids = rownames(dds),
                                            host=biomart_host,
                                            version=biomart_Ens_version,
                                            dataset=biomart_dataset)
  
  cat("saving...", precomputed_objects_file, "\n")
  cat("...into:", output_dir, "\n")
  save(dds, dds_filt, ensemblAnnot,  file = precomputed_objects_file)
  
} else{
  cat("RNA objects file '", precomputed_objects_filename, "' exist...loading\n")
  load(precomputed_objects_file)
}
## RNA objects file ' nk_tum_immunoedit_complete_dds_objects.RData ' exist...loading
#transformation after filtering
if(!file.exists(precomputed_transf_objects_file)){
  # transformation
  log2_norm_filt <- DESeq2::normTransform(dds_filt)
  vsd_filt <- DESeq2::vst(dds_filt, blind = TRUE) # blind = TRUE for QC
  #rld_filt <- rlog(dds_filt, blind = TRUE)
  #rld_filt - too big for >100 samples; skipping!
  #rld_filt <- rlog(dds_filt, blind = FALSE) # not blind to batch effects
  save(log2_norm_filt, vsd_filt,   
       file = precomputed_transf_objects_file)
} else{
  load(precomputed_transf_objects_file)
}

Parameters for dataset preparation:

Parameter Value Decription
experiment_name nk_tum_immunoedit_complete this can be a subset analysis - e.g. just batch1,…
experiment_design 1 used only to construct initial dds object
abs_filt_samples 3 # at least in N samples, which is a smallest group size
biomart_host http://nov2020.archive.ensembl.org
biomart_Ens_version Ensembl Genes 102
biomart_dataset mmusculus_gene_ensembl

Objects overview:

dds_objects_overview <- data.frame(dds_object = c("dds", "dds_filt"),
                                   n_samples = c(ncol(dds), ncol(dds_filt)),
                                   n_genes = c(nrow(dds), nrow(dds_filt)))

dds_objects_overview %>%
  kableExtra::kbl(caption = "dds objects overview") %>% 
  kableExtra::kable_classic(full_width = T)
dds objects overview
dds_object n_samples n_genes
dds 169 51149
dds_filt 169 15430
# DT::datatable(dds_objects_overview,
#           options = list(pageLength = 10),
#           caption = 'Table 1: Samples in validation set')

Exploratory Data Analysis - metadata

#import::from(.from = SummarizedExperiment, colData, assay)
import::from(.from = DT, datatable)
import::from(.from = ggplot2, .all=TRUE)
import::from(.from = DataExplorer, plot_bar)
#import::from(.from = tableone, CreateTableOne)
import::from(.from = dplyr, group_by, count)
experiment_name="nk_tum_immunoedit_complete"
output_dir <- paste0(results_dir, "/",experiment_name,"/") # "/home/rstudio/workspace/results_dir/nk_tum_immunoedit_complete/" #  
# results_dir defined in the nk_immunoediting.Rmd file

eda_plots_dir <- paste0(output_dir, "eda_plots/")
dir.create(eda_plots_dir)

# raw and filtered dds
precomputed_objects_filename <- paste0(experiment_name, "_dds_objects.RData") #"nk_tum_immunoedit_complete_dds_objects.RData" #
precomputed_objects_file <- file.path(output_dir, precomputed_objects_filename)

# log2 and vsd transformed
precomputed_transf_objects_filename <- "nk_tum_immunoedit_complete_log2_vsd_filt_objects.RData"
precomputed_transf_objects_file <- file.path(output_dir, precomputed_transf_objects_filename)

# check if object loaded if not load
if(!exists(precomputed_objects_file)){
  load(precomputed_objects_file)
  load(precomputed_transf_objects_file)
} 

metadata <- tibble::as_tibble(SummarizedExperiment::colData(dds_filt))

key_metadata <- metadata %>%
  dplyr::select(experiment, condition_tp,condition, timepoint_cell_harvesting, day_cell_harvesting, cell_line_label, technical_replicate) 

# saving metadata
openxlsx::write.xlsx(metadata, file = file.path(output_dir, paste0(experiment_name, "_metadata.xlsx")))

Metadata overview:

# 'KeyTable', ,'FixedHeader', 'Responsive', 'Scroller', 'ColReorder'
# to add show entries - https://stackoverflow.com/questions/64976684/how-to-have-buttons-and-a-show-entries-using-datatables-dt-in-rmarkdown

DT::datatable(data = metadata,
              caption = 'Metadata overview',
              filter = 'top',
              class = 'hover',
    extensions = c('Buttons'), options = list(
      pageLength = 5,
      #autoWidth = TRUE
      #fixedHeader = TRUE, 
      scrollX = TRUE,
      #scrollY = TRUE,
      #scrollY = 200,
      #deferRender = TRUE,
      #scroller = TRUE,
      #colReorder = TRUE,
      #keys = TRUE,
      searchHighlight = TRUE,
      dom = 'Blfrtip',
      buttons = list('copy', 'colvis', list(
        extend = 'collection',
        buttons = c('csv', 'excel', 'pdf'),
        text = 'Download'
      ))
  )
)
# 1. metadata overview
# 2. composition per experiment
# 3. relationship (table(x, y)) - to see how many variables we have in each category; data imbalance!?

# frequency of key variables ----
# fig.show='hide' - to supress plotting here
key_variables_freq <- DataExplorer::plot_bar(key_metadata, order_bar=TRUE, ggtheme = theme_bw(), title = "Frequency of key variables")
# table of variable frequency per experiment ----
key_variables_tableOne <- tableone::CreateTableOne(vars = colnames(dplyr::select(key_metadata, -experiment)), 
                           strata = c("experiment"), 
                           data = key_metadata)

# relate variables ----
# cat_cat_plot <- ggplot(data = key_metadata, color=cell_line_label) +
#   geom_count(mapping = aes(x = experiment, y = condition_tp)) #+   facet_wrap(~cell_line_label)

cat_cat_counts <- key_metadata %>% 
  dplyr::group_by(cell_line_label) %>%
  dplyr::count(experiment, condition_tp)

cat_cat_plot <- ggplot(data = cat_cat_counts, mapping = aes(x = experiment, y = condition_tp, group=cell_line_label)) +
  #     geom_tile(mapping = aes(fill = n))
  geom_point(mapping=aes(size=n, color=cell_line_label), position = position_dodge(width=0.5)) +
  scale_size(breaks = c(0,1,2,3,4,5,6)) +
  # ggrepel::geom_label_repel(aes(label = n),
  #                           box.padding   = 0.35, 
  #                           point.padding = 0.5,
  #                           segment.color = 'grey50') +
  ggtitle("Number of samples pre condition/experiment.") +
  theme_bw()

Frequency of key variable:

print(key_variables_freq$page_1)

ggsave(filename = paste0(eda_plots_dir, "key_variables_freq_plot.png"), 
       plot=key_variables_freq$page_1,
       width = 20, height = 20, units = "cm")

Overview of number of samples in different categories (experiment, condition,…).:

#print(key_variables_tableOne$CatTable)
tableone::kableone(key_variables_tableOne$CatTable,
                   caption = "Overview of number of samples in different categories (experiment, condition,...).") %>%
  kableExtra::kable_material(c("striped", "hover"))
Overview of number of samples in different categories (experiment, condition,…).
EXP9.4 EXP9.5 EXP9.6 EXP9.7 EXP9.8 p test
n 18 36 39 40 36
condition_tp (%) <0.001
Tumor_only_timepoint_0 0 ( 0.0) 0 ( 0.0) 0 ( 0.0) 0 ( 0.0) 6 (16.7)
Tumor_only_timepoint_1 6 (33.3) 12 (33.3) 12 (30.8) 12 (30.0) 6 (16.7)
Tumor_only_timepoint_2 0 ( 0.0) 0 ( 0.0) 4 (10.3) 4 (10.0) 6 (16.7)
Tumor_plus_NK_timepoint_1 6 (33.3) 12 (33.3) 12 (30.8) 12 (30.0) 0 ( 0.0)
Tumor_plus_NK_timepoint_2 6 (33.3) 12 (33.3) 11 (28.2) 12 (30.0) 0 ( 0.0)
Tumor_plus_WT_NK_timepoint_2 0 ( 0.0) 0 ( 0.0) 0 ( 0.0) 0 ( 0.0) 6 (16.7)
Tumor_plus_IFNg_timepoint_2 0 ( 0.0) 0 ( 0.0) 0 ( 0.0) 0 ( 0.0) 6 (16.7)
Tumor_plus_PrfKO_NK_timepoint_2 0 ( 0.0) 0 ( 0.0) 0 ( 0.0) 0 ( 0.0) 6 (16.7)
condition (%) <0.001
Tumor_only 6 (33.3) 12 (33.3) 16 (41.0) 16 (40.0) 18 (50.0)
Tumor_plus_NK 12 (66.7) 24 (66.7) 23 (59.0) 24 (60.0) 0 ( 0.0)
Tumor_plus_IFNg 0 ( 0.0) 0 ( 0.0) 0 ( 0.0) 0 ( 0.0) 6 (16.7)
Tumor_plus_WT_NK 0 ( 0.0) 0 ( 0.0) 0 ( 0.0) 0 ( 0.0) 6 (16.7)
Tumor_plus_PrfKO_NK 0 ( 0.0) 0 ( 0.0) 0 ( 0.0) 0 ( 0.0) 6 (16.7)
timepoint_cell_harvesting (%) <0.001
timepoint_0 0 ( 0.0) 0 ( 0.0) 0 ( 0.0) 0 ( 0.0) 6 (16.7)
timepoint_1 12 (66.7) 24 (66.7) 24 (61.5) 24 (60.0) 6 (16.7)
timepoint_2 6 (33.3) 12 (33.3) 15 (38.5) 16 (40.0) 24 (66.7)
day_cell_harvesting (%) <0.001
Day0 0 ( 0.0) 0 ( 0.0) 0 ( 0.0) 0 ( 0.0) 6 (16.7)
Day4 12 (66.7) 24 (66.7) 24 (61.5) 0 ( 0.0) 6 (16.7)
Day6 0 ( 0.0) 0 ( 0.0) 0 ( 0.0) 24 (60.0) 0 ( 0.0)
Day10 6 (33.3) 12 (33.3) 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
Day14 0 ( 0.0) 0 ( 0.0) 15 (38.5) 0 ( 0.0) 24 (66.7)
Day16 0 ( 0.0) 0 ( 0.0) 0 ( 0.0) 16 (40.0) 0 ( 0.0)
cell_line_label (%) <0.001
A 0 ( 0.0) 9 (25.0) 10 (25.6) 10 (25.0) 18 (50.0)
B 0 ( 0.0) 9 (25.0) 10 (25.6) 10 (25.0) 0 ( 0.0)
C 9 (50.0) 9 (25.0) 10 (25.6) 10 (25.0) 0 ( 0.0)
D 9 (50.0) 9 (25.0) 9 (23.1) 10 (25.0) 18 (50.0)
technical_replicate (%) 0.998
1 6 (33.3) 12 (33.3) 16 (41.0) 16 (40.0) 12 (33.3)
2 6 (33.3) 12 (33.3) 12 (30.8) 12 (30.0) 12 (33.3)
3 6 (33.3) 12 (33.3) 11 (28.2) 12 (30.0) 12 (33.3)

Plot of number of samples in each condition across different experiments:

print(cat_cat_plot)

ggsave(filename = paste0(eda_plots_dir, "key_variables_relate_plot.png"), 
       plot=cat_cat_plot,
       width = 20, height = 20, units = "cm")

Exploratory Data Analysis - expression

# import::from(.from = SummarizedExperiment, colData)
# import::from(.from = DT, datatable)
# import::from(.from = ggplot2, .all=TRUE)
# import::from(.from = DataExplorer, plot_bar)
# import::from(.from = tableone, CreateTableOne)
# import::from(.from = dplyr, group_by, count)

#import::from(.from = SummarizedExperiment, colData, assay)
import::from(.from = variancePartition, fitExtractVarPartModel, sortCols, plotVarPart)
import::from(.from = doParallel, registerDoParallel)
import::from(.from = parallel, makeCluster, stopCluster)

# import utils scripts
import::from(.from = here::here("utils/rnaSelectTopVarGenes.R"), "rnaSelectTopVarGenes", .character_only=TRUE)
import::from(.from = here::here("utils/edaFunctions.R"), "varPartitionEstimate", "generatePCA", "pcaExtractVariance", "pcaPlotVariance", "pcaCorrPCs", "pcaCorrPCsPlot", .character_only=TRUE)
experiment_name="nk_tum_immunoedit_complete"
output_dir <- paste0(results_dir, "/",experiment_name,"/") # "/home/rstudio/workspace/results_dir/nk_tum_immunoedit_complete/" 
# results_dir defined in the nk_immunoediting.Rmd file

eda_plots_dir <- paste0(output_dir, "eda_plots/")
dir.create(eda_plots_dir)

# raw and filtered dds
precomputed_objects_filename <-paste0(experiment_name, "_dds_objects.RData") # "nk_tum_immunoedit_complete_dds_objects.RData" 
precomputed_objects_file <- file.path(output_dir, precomputed_objects_filename)

# log2 and vsd transformed
precomputed_transf_objects_filename <- paste0(experiment_name, "_log2_vsd_filt_objects.RData") #"nk_tum_immunoedit_complete_log2_vsd_filt_objects.RData"
precomputed_transf_objects_file <- file.path(output_dir, precomputed_transf_objects_filename)

# check if object loaded if not load
if(!exists(precomputed_objects_file)){
  load(precomputed_objects_file)
  load(precomputed_transf_objects_file)
} 

metadata <- tibble::as_tibble(SummarizedExperiment::colData(dds_filt))

key_metadata <- metadata %>%
  dplyr::select(experiment, condition_tp,condition, timepoint_cell_harvesting, day_cell_harvesting, cell_line_label, technical_replicate) 

# saving metadata
#openxlsx::write.xlsx(metadata, file = file.path(output_dir, paste0(experiment_name, "_metadata.xlsx")))

# preparing vsd per experiment ----
experiments <- unique(as.character(vsd_filt$experiment))
vsd_filt_perExp <- purrr::map(.x = experiments, .f = function(experiment_name){
  vsd_filt[,vsd_filt$experiment == experiment_name]
}) %>% setNames(experiments)
#lapply(vsd_filt_perExp, dim)
cond_interest <- "condition_tp"   # column of interest for PCA plots etc.
cond_interest_varPart <- c("condition_tp", "condition", "timepoint_cell_harvesting", "cell_line_label", "technical_replicate", "experiment")
cond_interest_varPart_corr <- c("condition_tp", "cell_line_label", "experiment", "technical_replicate")
  
# #~  (1|technical_replicate) + (1|experiment) + (1|cell_line_label) + (1|condition_tp) 
fitform_partVariance <- ~(1 | technical_replicate) + (1 | experiment) + (1 | cell_line_label) + (1 | timepoint_cell_harvesting) + (1 | condition) 
padj_cutoff = 0.05
log2FC_cutoff = 0.58 #(FC=1.5); log2FC=1.0 # (FC=2)
var_expl_needed <- 0.6         # at least 60% variance explained needed
transf_object <- vsd_filt # which object to use for pca, variance partition etc. in case there is e.g. log2norm, rlog,...

Variance partition

# calculating overall variance partition ----
variance_partition_all <- varPartitionEstimate(transf_object = transf_object, 
                                              fitform_partVariance = fitform_partVariance,
                                              ntop_genes=1000,
                                              ncores = 12)
## Selecting 1000 Ntop genes based on variance.
# calculating variance partition per experiment ----
fitform_partVariance_perExperiment <- ~(1 | technical_replicate) + (1 | cell_line_label) + (1 | timepoint_cell_harvesting) + (1 | condition)
variance_partition_perExperiment <- purrr::map(.x=names(vsd_filt_perExp), .f=function(experiment_name){
  varPartitionEstimate(transf_object = vsd_filt_perExp[[experiment_name]], 
                                              fitform_partVariance = fitform_partVariance_perExperiment,
                                              ntop_genes=1000,
                                              ncores = 12)
}) %>% setNames(names(vsd_filt_perExp))
## Selecting 1000 Ntop genes based on variance.
## Selecting 1000 Ntop genes based on variance.
## Selecting 1000 Ntop genes based on variance.
## Selecting 1000 Ntop genes based on variance.
## Selecting 1000 Ntop genes based on variance.
#print(key_variables_tableOne$CatTable)
variance_partition_all_plot <- variance_partition_all$varPart_plot_annot + ggtitle("Variance partition all experiments")

print(variance_partition_all_plot)

ggsave(filename = paste0(eda_plots_dir, "variance_partition_all_plot.png"), 
       plot=variance_partition_all_plot,
       width = 20, height = 20, units = "cm")

Variance explained by different variables of interest. There seems to be a lot of variation coming from different experiments and cell lines. This will need to be accounted in the design or downstream batch effect removal.

#print(key_variables_tableOne$CatTable)
variance_partition_all$varPart_stats %>%
  kableExtra::kbl(caption="Variance partition table - all experiments") %>%
  kableExtra::kable_material(c("striped", "hover"))
Variance partition table - all experiments
variable median_varExplained mean_varExplained IQR_varExplained max_varExplained min_varExplained
experiment 26.5 35.9 52.3 99.7 0.0
cell_line_label 2.8 13.4 10.4 96.6 0.0
condition 0.8 7.7 8.0 82.8 0.0
timepoint_cell_harvesting 0.0 1.2 0.9 33.3 0.0
technical_replicate 0.0 0.3 0.2 6.8 0.0
Residuals 34.1 41.5 46.0 100.0 0.3

*Please note color coding varies between variance_partition_perExperiment

variance_partition_perExperiment_plots <- purrr::map(.x=names(variance_partition_perExperiment), .f = function(experiment_name){
  variance_partition_perExperiment[[experiment_name]]$varPart_plot_annot + ggtitle(paste0("Variance partition experiment - ", experiment_name))
}) %>% setNames(names(variance_partition_perExperiment))

variance_partition_perExperiment_plots <- purrr::map(.x=names(variance_partition_perExperiment), .f = function(experiment_name){
  print(variance_partition_perExperiment_plots[[experiment_name]])
  ggsave(filename = paste0(eda_plots_dir, paste0("variance_partition_", experiment_name,"_plot.png")), 
       plot=variance_partition_perExperiment_plots[[experiment_name]],
       width = 20, height = 20, units = "cm")
}) %>% setNames(names(variance_partition_perExperiment))

Principal Component Analysis (PCA)

# calculating how many components are needed ----
transf_object_counts_ntop <- rnaSelectTopVarGenes(SummarizedExperiment::assay(transf_object), ntop = 1000, type = "var")
## Selecting 1000 Ntop genes based on variance.
pca_object <- stats::prcomp(t(transf_object_counts_ntop), center = TRUE)
pca_results <- pcaExtractVariance(pca_object)
pca_scree_cumVarExpl <- pcaPlotVariance(pca_results, var_expl_needed = var_expl_needed)
min_components_needed <- which(pca_results$cummulative_variance > var_expl_needed)[1] # min components to get var_expl_needed 

# calculating components correlation with variables ----
#TODO:
# fix - correlation between PCs and variables of interest! - fix sample naming!
# PC_corr <- pcaCorrPCs(pca_object, metadata[cond_interest_varPart_corr], pcs = 1:min_components_needed, pval_exact = TRUE) 
# PC_corr_plot <- pcaCorrPCsPlot(PC_corr)

# calculating overall PCA ----
condition_tp_colors <- RColorBrewer::brewer.pal(length(unique(metadata$condition_tp)),"Dark2")
names(condition_tp_colors) <- levels(metadata$condition_tp)
    
pca_all <- generatePCA(transf_object = transf_object, 
                       cond_interest_varPart = cond_interest_varPart, 
                       color_variable = "condition_tp", 
                       shape_variable = "experiment",
                       ntop_genes = 1000) +
      ggtitle("Overall PCA") +
      ggplot2::scale_color_manual(values = condition_tp_colors)   
 
pca_perExperiment_plots <- purrr::map(.x=names(vsd_filt_perExp), .f = function(experiment_name){
    pca_plot <- generatePCA(transf_object = vsd_filt_perExp[[experiment_name]], 
                cond_interest_varPart = cond_interest_varPart, 
                color_variable = "condition_tp", 
                shape_variable = "cell_line_label",
                ntop_genes = 1000)
  
    pca_plot + ggtitle(experiment_name) +
      # make universal color, shape across the whole report!
      ggplot2::scale_shape_manual(values = c("A" = 16, "B" = 15, "C"=17, "D"=3)) +
      ggplot2::scale_color_manual(values = condition_tp_colors)   
}) %>% setNames(names(vsd_filt_perExp))

combined_pca_plot <- ggpubr::ggarrange(pca_perExperiment_plots$EXP9.4, 
                                       pca_perExperiment_plots$EXP9.5,
                                       pca_perExperiment_plots$EXP9.6,
                                       pca_perExperiment_plots$EXP9.7,
                                       pca_perExperiment_plots$EXP9.8, 
                                       nrow=3, ncol=2,
                                       common.legend = FALSE) #patchwork::wrap_plots(pca_perExperiment_plots, ncol=2)
print(pca_scree_cumVarExpl)

ggsave(filename = paste0(eda_plots_dir, "pca_scree_cumVarExpl_plot.png"), 
       plot=pca_scree_cumVarExpl,
       width = 20, height = 20, units = "cm")

Overall PCA plot:

print(pca_all)

ggsave(filename = paste0(eda_plots_dir, "pca_all_plot.png"), 
       plot=pca_all,
       width = 20, height = 20, units = "cm")

PCA plot per experiment:

print(combined_pca_plot)

ggsave(filename = paste0(eda_plots_dir, "pca_perExperiment_plot.png"), 
       plot=combined_pca_plot,
       width = 20, height = 20, units = "cm")

Results - TpNK_tp2_vs_Tonly_tp1

Copied completely from the “Prepare dataset.Rmd”

Trying to keep everythin in one place

The analysis is similar to the previous version, however here I don’t do the correction for the batch correction between AB and CD

# importing only key functions that are actually used - not to polute namespace!
import::from(.from = readr, read_csv, cols)
import::from(magrittr, "%>%")
import::from(dplyr, mutate, select, filter, rename, arrange, desc, group_by, summarise, ungroup)  # dplyr_mutate = mutate
import::from(purrr, map)
import::from(future, plan, multisession, sequential)
import::from(furrr, furrr_options, future_map2)
import::from(ggplot2, .all=TRUE) # importing all as there is too many
import::from(grid, gpar) # needed in complexheatmap
import::from(kableExtra, kable_styling, kbl)

import::from(.from = GenomicFeatures, makeTxDbFromGFF)
import::from(.from = AnnotationDbi, annot_db_keys = keys, annot_db_select = select)
import::from(.from = DESeq2, .all=TRUE)
import::from(.from = tximport, tximport)

import::from(.from = here::here("utils/filterDatasets.R"), "filterDatasets", .character_only=TRUE) # used for filtering
import::from(.from = here::here("utils/generateEnsemblAnnotation.R"), "generateEnsemblAnnotation", .character_only=TRUE) # used for filtering
#project setup
# loading config file ----
config_file <- file.path( "nk_tum_immunoediting_config.yaml")  # params$config_file; params not found? ; change to project_config.yaml
config <- yaml::read_yaml(config_file)
#config <- yaml::read_yaml(params$config_file)

experiment_name="nk_tum_immunoedit_complete" # this can be a subset analysis - e.g. just batch1,...
#experiment_name="nk_tum_immunoedit_complete_ab2_AB_CD_sep" # this can be a subset analysis - e.g. just batch1,...
experiment_design="1"  # used only to construct initial dds object
abs_filt_samples=3

# parameters for annotation
biomart_host = "http://nov2020.archive.ensembl.org"
#biomart_host = "https://www.ensembl.org"
biomart_Ens_version = "Ensembl Genes 102"
biomart_dataset="mmusculus_gene_ensembl"

# directories
output_dir = paste0("./results_dir", "/",experiment_name,"/")
dir.create(output_dir)

Preparing metadata and expression data and combining into a dds (DESeqDataSet object) that is ready for analyses. dds object is further pre-filtered to remove lowly expressed genes (FPM (fragments per million mapped fragments) > 1 in at least 3 samples). Salmon quantification (quant.sf) transcript abundance estimates (transcript expression) is imported and converted to gene expression using tximport.

# Preparing metadata
rnaseq_metadata_file <- here::here(config$metadata$rnaseq$rnaseq_metadata)
rnaseq_metadata_df_raw <- readr::read_csv(file = rnaseq_metadata_file)  # 133 samples; 42 samples from other project?
quantseq_files <- here::here(config$metadata$rnaseq$rnaseq_salmon_files)

# salmon quant files
quant_files_md5sums_raw <- readr::read_table(file = quantseq_files, col_names = c("md5sum", "file_path"))
quant_files_md5sums <- quant_files_md5sums_raw %>%
  dplyr::mutate(absolute_quant_files_path = gsub(pattern = "\\./", replacement = paste0(dirname(quantseq_files), "/"), file_path),
                sample_name = gsub(pattern = "(\\./)(S_.+)(_0_.+)", replacement = "\\2", file_path)) %>%
  # for batch3
  dplyr::mutate(sample_name = gsub(pattern = "(\\./)(R.+)(_S.+)", replacement = "\\2", sample_name)) %>%
  # EK were samples from other experiment - they were removed from quant.sf list, but in case remove them here as well!
  dplyr::filter(!grepl(pattern = "\\./EK.+", x = sample_name)) %>%
  dplyr::mutate(bsf_sample_name = gsub(pattern = "(\\./)(S_.+)(_quant/quant.sf)", replacement = "\\2", x = file_path)) %>%
  # for batch 3
  dplyr::mutate(bsf_sample_name = gsub(pattern = "(\\./)(R.+_S.+)(_quant/quant.sf)", replacement = "\\2", x = bsf_sample_name))

# experiment info - match experiment to run
experiment_info_batch1_2_raw <- readr::read_csv("datasets/metadata/rnaseq_merge_sample_sheet.csv", col_types =cols(.default = "c"))
experiment_info_batch3_raw <- readr::read_csv("datasets/metadata/rnaseq_sample_sheet_batch3.csv", col_types = cols(.default = "c"))

experiment_info_raw <- dplyr::bind_rows(experiment_info_batch1_2_raw, experiment_info_batch3_raw)

# EK samples are from different experiment
experiment_info <- experiment_info_raw %>%
  dplyr::filter(!grepl(pattern = "EK.+", x = original_sample_name)) %>%
  dplyr::rename(library_name = experiment,
                bsf_sample_name = sample_name) %>%
  # fixing library_name for experiment EXP9.8
  dplyr::mutate(library_name = dplyr::if_else(library_name == "BSF_1241", "R0128_L5464", library_name)) %>%
  dplyr::mutate(sample_name_unique = dplyr::case_when(grepl(pattern = "^R", x=original_sample_name) ~ paste0(original_sample_name, "_", library_name),
                                                      TRUE ~ paste0("S_", original_sample_name, "_", library_name)))
# loading metadata and adding experiment and salmon data
rnaseq_metadata_df <- rnaseq_metadata_df_raw %>%
  dplyr::mutate(condition_complete = condition) %>%
  dplyr::mutate(condition = gsub(pattern = "(.+)(_Day.+)", replacement = "\\1", condition_complete)) %>%
  dplyr::mutate(condition = gsub(pattern = "\\+", replacement = "_plus_", condition),
                timepoint_cell_harvesting = gsub(pattern = " ", replacement = "_", timepoint_cell_harvesting),
                experiment = gsub(pattern = " ", replacement = "", original_experiment)) %>%
  dplyr::mutate(sample_name_unique = dplyr::case_when(grepl(pattern = "^R", x=sample_name) ~ paste0(sample_name, "_", library_name),
                   TRUE ~ paste0("S_", sample_name, "_", library_name)))

quant_files_md5sums_annot <- quant_files_md5sums %>%
  dplyr::left_join(., experiment_info, by = "bsf_sample_name") %>%
  dplyr::select(-sample_name, -library_name)

rnaseq_metadata_df_annot <- rnaseq_metadata_df %>%
  dplyr::left_join(., quant_files_md5sums_annot, by="sample_name_unique") %>%
  dplyr::mutate(original_sample_name = sample_name) %>%
  dplyr::mutate(sample_name = sample_name_unique) %>%
  dplyr::mutate(filenames = sample_name_unique)

#table(rnaseq_metadata_df_annot$original_experiment)
#table(rnaseq_metadata_df_annot$cell_line_label)
rnaseq_metadata_df_annot_reduced <- rnaseq_metadata_df_annot %>%
  #dplyr::filter(original_experiment == "EXP9_6") %>%
  #dplyr::filter(condition %in% c("Tumor_only", "Tumor_plus_NK_Day14")) %>%
  dplyr::mutate(technical_replicate = factor(technical_replicate, levels = c("1", "2", "3")),
                condition = factor(condition, levels = c("Tumor_only", "Tumor_plus_NK", "Tumor_plus_IFNg", "Tumor_plus_WT_NK", "Tumor_plus_PrfKO_NK")),
                timepoint_cell_harvesting = factor(timepoint_cell_harvesting, levels = c("timepoint_0", "timepoint_1", "timepoint_2")),
                cell_line_label = as.factor(cell_line_label),
                experiment = factor(experiment, levels = c("EXP9.4", "EXP9.5", "EXP9.6", "EXP9.7", "EXP9.8")),
                condition_tp = paste0(condition,"_",timepoint_cell_harvesting)) %>%
  dplyr::mutate(condition_tp = factor(condition_tp, levels = c("Tumor_only_timepoint_0", 
                                                               "Tumor_only_timepoint_1", "Tumor_only_timepoint_2",
                                                               "Tumor_plus_NK_timepoint_1", "Tumor_plus_NK_timepoint_2",
                                                               "Tumor_plus_WT_NK_timepoint_2",
                                                               "Tumor_plus_IFNg_timepoint_2", "Tumor_plus_PrfKO_NK_timepoint_2"))) %>%
  #fixing day_cell_harvesting 
  dplyr::mutate(day_cell_harvesting = gsub(pattern = " ", replacement = "", day_cell_harvesting)) %>%
  dplyr::mutate(day_cell_harvesting = factor(day_cell_harvesting, levels=c("Day0", "Day4", "Day6", "Day10", "Day14", "Day16")))

metadata <- rnaseq_metadata_df_annot_reduced #cond_data
#rm(reseq_metadata)
rownames(metadata) <- metadata$filenames

# use alternative sample names for multiQC
# renaming for the multiQC report
# metadata_multiqc <- metadata %>%
#   dplyr::select(bsf_sample_name, experiment, technical_replicate, cell_line_label, condition, timepoint_cell_harvesting) %>%
#   dplyr::mutate(cell_line_label = paste0("cl", cell_line_label), 
#                 condition = gsub(pattern = "Tumor", replacement = "T", x = condition),
#                 timepoint_cell_harvesting = gsub(pattern = "timepoint", replacement = "tp", x=timepoint_cell_harvesting),
#                 technical_replicate = paste0("repl",technical_replicate)) %>%
#   dplyr::mutate(alt_name = paste(experiment, cell_line_label, technical_replicate, condition, timepoint_cell_harvesting, sep="-")) %>%
#   dplyr::select(bsf_sample_name, alt_name)
# 
# readr::write_tsv(metadata_multiqc, file = paste0(OUTPUT_DIR, experiment_name, "_metadata_multiqc.tsv"))
#TODO:
# [ ] - add md5sums for tx2gene_file

data_dir <- file.path("datasets") 
annotation_dir = paste0(data_dir, "/annotations/")

gtf_file <- file.path(annotation_dir, "Mus_musculus.GRCm38.102.gtf") # needed only once to generate tx2gene.tsv
tx2gene_file <- file.path(annotation_dir, "EnsDb.Mmusculus.v102_tx2gene.tsv")
tx2gene_file_md5sum <- file.path(annotation_dir, "EnsDb.Mmusculus.v102_tx2gene.md5sum")

if (!file.exists(tx2gene_file)) {
  message(tx2gene_file, " does not exist: generating!")
  txDB <- GenomicFeatures::makeTxDbFromGFF(gtf_file, format="gtf")
  
  # AnnotationDbi::keys
  tx_name <- annot_db_keys(txDB, keytype="TXNAME")
  
  # AnnotationDbi::select
  tx2gene_gtf <- annot_db_select(txDB,
                                 keys = tx_name,
                                 columns="GENEID",
                                 keytype = "TXNAME")
  
  
  # saving tx2gene into destdir. Potentially there is a typo here. The name should be tx2gene, not _gtf
  write.table(tx2gene_gtf,
              file = tx2gene_file,
              sep = "\t",
              col.names = FALSE,
              row.names = FALSE,
              quote = FALSE)
  
  tx2gene_md5sum <- digest::digest(tx2gene_file, file=TRUE, algo="md5")
  write.table(paste(tx2gene_md5sum, basename(tx2gene_file), collapse = "\t"),
              file = tx2gene_file_md5sum,
              sep = "\t",
              col.names = FALSE,
              row.names = FALSE,
              quote = FALSE)
  
} else {
  message(tx2gene_file, " exists: loading!")
  tx2gene <- readr::read_tsv(tx2gene_file,
                             col_names = c("tx_id", "gene_id"),
                             show_col_types = FALSE)
}
precomputed_objects_filename <- paste0("nk_tum_immunoedit_complete", "_dds_objects.RData")
precomputed_objects_file <- file.path(output_dir, precomputed_objects_filename)

precomputed_transf_objects_filename <- paste0("nk_tum_immunoedit_complete", "_log2_vsd_filt_objects.RData")
precomputed_transf_objects_file <- file.path(output_dir, precomputed_transf_objects_filename)

if(!file.exists(precomputed_objects_file)){
  cat("RNA objects file '", precomputed_objects_filename, "' does not exist in the OUTPUT_DIR...\n")
  cat("generating", precomputed_objects_filename, "\n")
  # Generating dds and filtered dds objects ----
  #require(DESeq2)
  # here there is a single sample so we use ~1.
  # expect a warning that there is only a single sample...
  
  # generating dds object ----
  # txi_object <- generateTxi(metadata_object=metadata,
  #                           column_names=c("absolute_quant_files_path", "filenames"),
  #                           tx2gene = tx2gene)
  
  quant_files <- metadata$absolute_quant_files_path
  names(quant_files) <- metadata$sample_name
  
  txi_object <- tximport::tximport(quant_files,
                         type="salmon",
                         tx2gene = tx2gene,
                         ignoreTxVersion = TRUE)
  
  sum(rownames(metadata) == colnames(txi_object$counts)) # check if names match between metadata and counts data
  cat("... txi_object\n")
  
  dds <- DESeq2::DESeqDataSetFromTximport(txi_object, 
                                          colData = metadata, 
                                          design = as.formula(paste0("~", experiment_design)))
  
  cat("... dds\n")
  dds <- DESeq2::estimateSizeFactors(dds) # Isn't the data already normalized by Salmon?
  dds <- DESeq2::DESeq(dds, minReplicatesForReplace=Inf) # do not replace outliers based on replicates
  
  # filtering dds
  cat("... dds filtering\n")
  dds_filt <- filterDatasets(dds, abs_filt = TRUE, 
                             abs_filt_samples = abs_filt_samples) # at least in N samples, which is a smallest group size
  dds_filt <- DESeq2::estimateSizeFactors(dds_filt)

  # Generate annotation file using biomaRt
  #FIXME:
  # Issues with Curl when using https:// and http:// throws a warning:
  #  Warning: Ensembl will soon enforce the use of https.
  #   Ensure the 'host' argument includes "https://"
  cat("... fetching annotation from biomart\n")
  ensemblAnnot <- generateEnsemblAnnotation(ensembl_ids = rownames(dds),
                                            host=biomart_host,
                                            version=biomart_Ens_version,
                                            dataset=biomart_dataset)
  
  cat("saving...", precomputed_objects_file, "\n")
  cat("...into:", output_dir, "\n")
  save(dds, dds_filt, ensemblAnnot,  file = precomputed_objects_file)
  
} else{
  cat("RNA objects file '", precomputed_objects_filename, "' exist...loading\n")
  load(precomputed_objects_file)
}
## RNA objects file ' nk_tum_immunoedit_complete_dds_objects.RData ' exist...loading
#transformation after filtering
if(!file.exists(precomputed_transf_objects_file)){
  # transformation
  log2_norm_filt <- DESeq2::normTransform(dds_filt)
  vsd_filt <- DESeq2::vst(dds_filt, blind = TRUE) # blind = TRUE for QC
  #rld_filt <- rlog(dds_filt, blind = TRUE)
  #rld_filt - too big for >100 samples; skipping!
  #rld_filt <- rlog(dds_filt, blind = FALSE) # not blind to batch effects
  save(log2_norm_filt, vsd_filt,   
       file = precomputed_transf_objects_file)
} else{
  load(precomputed_transf_objects_file)
}

Parameters for dataset preparation:

Parameter Value Decription
experiment_name nk_tum_immunoedit_complete this can be a subset analysis - e.g. just batch1,…
experiment_design 1 used only to construct initial dds object
abs_filt_samples 3 # at least in N samples, which is a smallest group size
biomart_host http://nov2020.archive.ensembl.org
biomart_Ens_version Ensembl Genes 102
biomart_dataset mmusculus_gene_ensembl

Objects overview:

dds_objects_overview <- data.frame(dds_object = c("dds", "dds_filt"),
                                   n_samples = c(ncol(dds), ncol(dds_filt)),
                                   n_genes = c(nrow(dds), nrow(dds_filt)))

dds_objects_overview %>%
  kableExtra::kbl(caption = "dds objects overview") %>% 
  kableExtra::kable_classic(full_width = T)
dds objects overview
dds_object n_samples n_genes
dds 169 51149
dds_filt 169 15430
# DT::datatable(dds_objects_overview,
#           options = list(pageLength = 10),
#           caption = 'Table 1: Samples in validation set')

End of the data_prep steps

#import::from(.from = SummarizedExperiment, colData, assay)
import::from(.from = variancePartition, fitExtractVarPartModel, sortCols, plotVarPart)
import::from(.from = doParallel, registerDoParallel)
import::from(.from = parallel, makeCluster, stopCluster)

# import utils scripts
import::from(.from = here::here("utils/filterDatasets.R"), "filterDatasets", .character_only=TRUE) # used for filtering
import::from(.from = here::here("utils/rnaSelectTopVarGenes.R"), "rnaSelectTopVarGenes", .character_only=TRUE)
import::from(.from = here::here("utils/edaFunctions.R"), "varPartitionEstimate", "generatePCA", "pcaExtractVariance", "pcaPlotVariance", "pcaCorrPCs", "pcaCorrPCsPlot", "generatePCA_plus_shape", .character_only=TRUE)
import::from(.from = here::here("utils/generateResults.R"), "generateResults_upd", .character_only=TRUE) # 
import::from(.from = here::here("utils/plotDegResults.R"), "plotVolcano","plotVolcano_repel", .character_only=TRUE)

library(ggrepel)

Differential expression analysis

Comparison between Tumor_plus_NK vs Tumor_only - specifically Tumor_plus_NK_timepoint_2 against Tumor_only_timepoint_1. Tumor_plus_WT_NK_timepoint_2 was renamed to Tumor_plus_NK_timepoint_2

Steps:

  • subsetting dds_filt to contain only condition_tp of interest (Tumor_only_timepoint_1, Tumor_plus_NK_timepoint_2 = Tumor_plus_WT_NK_timepoint_2)
  • filter (again) for lowly expressed genes
  • results with differentially expressed genes (DEG) and significantly DEG at a selected cutoff
  • enrichment analysis (over-representation analysis) of the significantly DEG genes; GOBP, GOCC and GOMF terms enrichment
abs_filt_samples=3
padj_cutoff = 0.05
log2FC_cutoff = 0.58 #(FC=1.5); log2FC=1.0 # (FC=2)
var_expl_needed <- 0.6         # at least 60% variance explained needed

# compare Tumor_only between timepoints!
# condition_tp_subset <- c("Tumor_only_timepoint_0", 
#                          "Tumor_only_timepoint_1", 
#                          "Tumor_only_timepoint_2", 
#                          "Tumor_plus_NK_timepoint_1", 
#                          "Tumor_plus_NK_timepoint_2",
#                          "Tumor_plus_WT_NK_timepoint_2")

condition_tp_subset <- c("Tumor_only_timepoint_1",
                         "Tumor_plus_NK_timepoint_2",
                         "Tumor_plus_NK_timepoint_1",
                         "Tumor_plus_WT_NK_timepoint_2")

genes_of_interest <- list("Denn2b"="ENSMUSG00000031024",
                          "Gbp6"="ENSMUSG00000104713",
                          "H2-K1"="ENSMUSG00000061232",
                          "Hs3st2"="ENSMUSG00000046321",
                          "Htra3"="ENSMUSG00000029096",
                          "Ifi27"="ENSMUSG00000064215",
                          "Igtp"="ENSMUSG00000078853",
                          "Irf1"="ENSMUSG00000018899",
                          "Lgals3bp"="ENSMUSG00000033880",
                          "Ly6a"="ENSMUSG00000075602",
                          "Plaat3"="ENSMUSG00000060675",
                          "Rtp4"="ENSMUSG00000033355",
                          "Serpina3f"="ENSMUSG00000066363",
                          "Stat1"="ENSMUSG00000026104")

genes_of_interest_exp9.4_9.7 <- list(
                          'Ifi44'     ="ENSMUSG00000028037", 
                          'Ccl5'      ="ENSMUSG00000035042",
                          'Iigp1'     ="ENSMUSG00000054072",
                          'Serpina3f' ="ENSMUSG00000066363",
                          'Ly6a'      ="ENSMUSG00000075602",
                          'Plaat3'    ="ENSMUSG00000060675",
                          'Lgals3bp'  ="ENSMUSG00000033880",
                          'Ifi27'     ="ENSMUSG00000064215",
                          'Gbp6'      ="ENSMUSG00000104713",
                          'Rtp4'      ="ENSMUSG00000033355",
                          'Stat1'     ="ENSMUSG00000026104",
                          'Tap1'      ="ENSMUSG00000037321",
                          'H2-K1'     ="ENSMUSG00000061232",
                          'B2m'       ="ENSMUSG00000060802",
                          'Igtp'      ="ENSMUSG00000078853",
                          'Gtf2ird1'  ="ENSMUSG00000023079",
                          'Cdc42ep4'  ="ENSMUSG00000041598",
                          'Hoxb6'     ="ENSMUSG00000000690",
                          'Bfsp2'     ="ENSMUSG00000032556",
                          'Sox6'      ="ENSMUSG00000051910",
                          'Tgm2'      ="ENSMUSG00000037820",
                          'Adprhl1'   ="ENSMUSG00000031448")

# additional parameters ----

if(exists("deg_design")) {rm(deg_design)}
deg_design <- as.formula("~ experiment + cell_line_label + condition_tp")


# # raw and filtered dds
# precomputed_objects_filename <- paste0(experiment_name, "_dds_objects.RData")
# precomputed_objects_file <- file.path(output_dir, precomputed_objects_filename)
# 
# # log2 and vsd transformed
# precomputed_transf_objects_filename <- paste0(experiment_name, "_log2_vsd_filt_objects.RData")
# precomputed_transf_objects_file <- file.path(output_dir, precomputed_transf_objects_filename)
# 
# # check if object loaded if not load
# if(!exists(precomputed_objects_file)){
#   load(precomputed_objects_file)
#   load(precomputed_transf_objects_file)
# } 
# preparing dds and vsd objects for the main project comparison 
# We use only EXP 9.4 - 9.7. EXP 9.8 is excluded.
# Preparing AB subset. 
# defining parameters

deg_name <- "TpNK_tp2_vs_Tonly_tp1_AB"
deg_dir <- file.path(output_dir, paste0("deg_", deg_name, "/"))
dir.create(deg_dir)

precomputed_objects_deg_filename <- paste0(deg_name, "_dds_objects.RData")
precomputed_objects_deg_file <- file.path(deg_dir, precomputed_objects_deg_filename)

precomputed_transf_objects_deg_filename <- paste0(deg_name, "_log2_vsd_filt_objects.RData")
precomputed_transf_objects_deg_file <- file.path(deg_dir, precomputed_transf_objects_deg_filename)

experiment_subset <- c("EXP9.4", "EXP9.5", "EXP9.6", "EXP9.7")


# just in case remove any existing subsets
if(exists("dds_subset")) {rm(dds_subset)}
if(exists("dds_subset_filt")) {rm(dds_subset_filt)}
if(exists("vsd_subset")) {rm(vsd_subset)}

if(!file.exists(precomputed_objects_deg_file)){
  cat("RNA objects file '", precomputed_objects_deg_filename, "' does not exist in the OUTPUT_DIR...\n")
  cat("generating", precomputed_objects_deg_filename, "\n")

  dds_subset <- dds_filt[ , dds_filt$condition_tp %in% condition_tp_subset]  
  dds_subset <- dds_subset[ , dds_subset$experiment %in% experiment_subset]
  dds_subset <- dds_subset[ , dds_subset$cell_line_label %in% c("A", "B")]
  
  # fixing Tumor_plus_WT_NK_timepoint_2 -> Tumor_plus_NK_timepoint_2
  dds_subset$condition_tp <- droplevels(dds_subset$condition_tp)
  table(dds_subset$condition_tp)
  
  dds_subset$experiment <- droplevels(dds_subset$experiment)
  table(dds_subset$experiment)
  
  dds_subset$cell_line_label <- droplevels(dds_subset$cell_line_label)
  table(dds_subset$cell_line_label)
  
  # filtering lowly expressed genes
  dds_subset_filt <- filterDatasets(dds_subset, 
                                    abs_filt = TRUE, 
                                    abs_filt_samples = abs_filt_samples) # at least in N samples, which is a smallest group size

  cat("... dds\n")
  design(dds_subset_filt) <- deg_design
  dds_subset_filt <- DESeq2::estimateSizeFactors(dds_subset_filt)
  dds_subset_filt <- DESeq2::DESeq(dds_subset_filt) # do not replace outliers based on replicates
  
  cat("saving...", precomputed_objects_file, "\n")
  cat("...into:", output_dir,deg_name,"\n")
  save(dds_subset, dds_subset_filt, ensemblAnnot,  file = precomputed_objects_deg_file)
  
} else{
  cat("RNA objects file '", precomputed_objects_deg_filename, "' exist...loading\n")
  load(precomputed_objects_deg_file)
}
## RNA objects file ' TpNK_tp2_vs_Tonly_tp1_AB_dds_objects.RData ' exist...loading
#transformation after filtering
if(!file.exists(precomputed_transf_objects_deg_file)){
  # transformation
  log2_norm_subset_filt <- DESeq2::normTransform(dds_subset_filt)
  vsd_subset_filt <- DESeq2::vst(dds_subset_filt, blind = FALSE) # using blind=FALSE utilize design info; blind = TRUE for QC
  #rld_filt <- DESeq2::rlog(dds_subset_filt, blind = FALSE) # more robust to differences in sequencing depth!
  #rld_filt - too big for >100 samples; skipping!
  #rld_filt <- rlog(dds_filt, blind = FALSE) # not blind to batch effects
  save(log2_norm_subset_filt, vsd_subset_filt,   
       file = precomputed_transf_objects_deg_file)
} else{
  load(precomputed_transf_objects_deg_file)
}
#get the counts table for diffTFs  AB cell lines
fl_smpl_nm <- gsub("_Day\\d\\d","",dds_subset@colData@listData[["full_sample_name"]])
fl_smpl_nm <- gsub("_\\d_","_",fl_smpl_nm)

sample_name <- paste0(dds_subset@colData@listData[["original_experiment"]], "_",
                      fl_smpl_nm, "_",
                      dds_subset@colData@listData[["day_cell_harvesting"]], "_REP",
                      dds_subset@colData@listData[["technical_replicate"]])

sample_name <- gsub("^EXP\\s9.", "EXP9_", sample_name)
sample_name <- gsub("\\+", "_plus_", sample_name)
sample_name <- gsub("_Tumor_only_Day\\d_", "_Tumor_only_", sample_name)

#These samples from RNA-seq are missing in ATAC data.
#EXP9_6_13G_Tumor_plus_NK_Day14_REP1 is missing in ./
#EXP9_6_13G_Tumor_plus_NK_Day14_REP2 is missing in ./
#EXP9_6_13G_Tumor_plus_NK_Day14_REP3 is missing in ./
#EXP9_6_13H_Tumor_plus_NK_Day14_REP1 is missing in ./
#EXP9_6_13H_Tumor_plus_NK_Day14_REP2 is missing in ./
#EXP9_6_13H_Tumor_plus_NK_Day14_REP3 is missing in ./
#
#Instead there is Day_10 version.
#
#I'm going to replace in the RNA-seq count table the day 14 with day 10, so that samples match.

sample_name <- gsub("EXP9_6_13G_Tumor_plus_NK_Day14", "EXP9_6_13G_Tumor_plus_NK_Day10", sample_name)
sample_name <- gsub("EXP9_6_13H_Tumor_plus_NK_Day14", "EXP9_6_13H_Tumor_plus_NK_Day10", sample_name)
col_IDS <- c("ENSEMBL", sample_name)
RNA_seq_raw_counts <- dds_subset@assays@data@listData[["counts"]]
RNA_seq_raw_counts <- cbind(row.names(RNA_seq_raw_counts), RNA_seq_raw_counts)
RNA_seq_raw_counts <- rbind(col_IDS,RNA_seq_raw_counts)

write.table(RNA_seq_raw_counts, file = "~/workspace/RNA_seq_raw_counts_AB.tsv",
            sep ="\t", quote = FALSE, row.names = FALSE, col.names = FALSE)

# Building the sample table
sampleID_list <- list("sampleID" = sample_name)
bamReads_list <- list("bamReads" = paste0("/home/aleksandr_b/bioinf_isilon/core_bioinformatics_unit/Internal/aleksandr/projects/ab3_20230220_nk_tumor_project/data_ATACSeq_nfcore_run/results/bwa/merged_library/", sample_name, ".mLb.clN.sorted.bam"))
cond_summ <- sample_name
cond_summ[stringr::str_detect(cond_summ, pattern = "_Tumor_only_")] <- "Tumor_only_TP_1"
cond_summ[stringr::str_detect(cond_summ, pattern = "_Tumor_plus_NK")] <- "Tumor_pls_NK_TP2"
conditionSummary_list <- list("conditionSummary" = cond_summ)
experiment_id_list <- list("experiment_id" = stringr::str_extract(sample_name, "EXP\\d_\\d"))

sampleData.tsv <- data.frame(sampleID_list,
                             bamReads_list,
                             conditionSummary_list,
                             experiment_id_list)
write.table(sampleData.tsv, file = "~/workspace/sampleData.tsv",
            sep ="\t", quote = FALSE, row.names = FALSE)
#print(key_variables_tableOne$CatTable)
rm(key_metadata_subset, key_variables_tableOne) # in case this exists from previus runs
key_metadata_subset <- as.data.frame(colData(dds_subset)) %>%
  dplyr::select(experiment, condition_tp, cell_line_label, technical_replicate) 

key_variables_tableOne <- tableone::CreateTableOne(vars = colnames(dplyr::select(key_metadata_subset, -condition_tp)), 
                           strata = c("condition_tp"), 
                           data = key_metadata_subset)

tableone::kableone(key_variables_tableOne$CatTable,
                   caption = "Overview of number of samples in different categories (experiment, condition,...).") %>%
  kableExtra::kable_material(c("striped", "hover"))
Overview of number of samples in different categories (experiment, condition,…).
Tumor_only_timepoint_1 Tumor_plus_NK_timepoint_1 Tumor_plus_NK_timepoint_2 p test
n 18 18 18
experiment (%) 1.000
EXP9.5 6 (33.3) 6 (33.3) 6 (33.3)
EXP9.6 6 (33.3) 6 (33.3) 6 (33.3)
EXP9.7 6 (33.3) 6 (33.3) 6 (33.3)
cell_line_label = B (%) 9 (50.0) 9 (50.0) 9 (50.0) 1.000
technical_replicate (%) 1.000
1 6 (33.3) 6 (33.3) 6 (33.3)
2 6 (33.3) 6 (33.3) 6 (33.3)
3 6 (33.3) 6 (33.3) 6 (33.3)
#TODO:
# - plot images after removing batch effects!
# EDA and visualize plots before and after batch effect correction

# transf_object <- vsd_subset_filt
# pca_deg <- generatePCA(transf_object = transf_object, 
#                        cond_interest_varPart = c("condition_tp", "cell_line_label", "experiment"), 
#                        color_variable = "condition_tp", 
#                        shape_variable = "experiment",
#                        ntop_genes = 1000) +
#   ggtitle("Original dataset") +
#  geom_text_repel(aes(label = transf_object$sample_name),
#                   box.padding = 3)
# 
# 
# 
# pca_deg_plotCellLabel <- generatePCA(transf_object = transf_object, 
#                        cond_interest_varPart = c("condition_tp", "cell_line_label", "experiment"), 
#                        color_variable = "condition_tp", 
#                        shape_variable = "cell_line_label",
#                        ntop_genes = 1000) +
#   ggtitle("Original dataset")
# 
# # Remove batch effect
# transf_batch_NObatch_experiment <- vsd_subset_filt
# transf_batch_NObatch_experiment_count <- limma::removeBatchEffect(SummarizedExperiment::assay(transf_batch_NObatch_experiment), transf_batch_NObatch_experiment$experiment)
# SummarizedExperiment::assay(transf_batch_NObatch_experiment) <- transf_batch_NObatch_experiment_count
# 
# pca_deg_NObatch_experiment <- generatePCA(transf_object = transf_batch_NObatch_experiment, 
#                        cond_interest_varPart = c("condition_tp", "cell_line_label", "experiment"), 
#                        color_variable = "condition_tp", 
#                        shape_variable = "experiment",
#                        ntop_genes = 1000) +
#   ggtitle("Removed batchEffect - experiment")
# 
# pca_deg_NObatch_experiment_plotCellLabel <- generatePCA(transf_object = transf_batch_NObatch_experiment, 
#                        cond_interest_varPart = c("condition_tp", "cell_line_label", "experiment"), 
#                        color_variable = "condition_tp", 
#                        shape_variable = "cell_line_label",
#                        ntop_genes = 1000) +
#   ggtitle("Removed batchEffect - experiment")
# 
# #PCA_batch_merged <- ggpubr::ggarrange(pca_deg, pca_deg_NObatch_experiment, common.legend = TRUE)
# 
# 
# PCA_batch_merged <- ggpubr::ggarrange(pca_deg, 
#                                       pca_deg_NObatch_experiment,
#                                       common.legend = TRUE)
# 
# PCA_batch_highlightCellLines_merged <- ggpubr::ggarrange(pca_deg_plotCellLabel, 
#                                       pca_deg_NObatch_experiment_plotCellLabel,
#                                       common.legend = TRUE)


###### Plots for publication #######
transf_object <- vsd_subset_filt

pca_deg <- generatePCA_plus_shape(transf_object = transf_object, 
                       cond_interest_varPart = c("cell_line_label", "experiment","condition_tp"), 
                       color_variable = "condition_tp", 
                       shape_variable = "Cell_line_label_and_exp_id",
                       ntop_genes = 1000)+
  ggtitle("Original dataset") +
  scale_shape_manual(values = c(0,1,2,15,16,17)) +
  scale_color_manual(values = c("#DD3344", "#FF9F1C" ,"#553388"))
pca_deg

# remove batch effect
transf_batch_NObatch_experiment <- vsd_subset_filt
transf_batch_NObatch_experiment_count <- limma::removeBatchEffect(SummarizedExperiment::assay(transf_batch_NObatch_experiment), transf_batch_NObatch_experiment$experiment)
SummarizedExperiment::assay(transf_batch_NObatch_experiment) <- transf_batch_NObatch_experiment_count


pca_deg_NObatch_experiment <- generatePCA_plus_shape(transf_object = transf_batch_NObatch_experiment, 
                       cond_interest_varPart = c("cell_line_label", "experiment","condition_tp"), 
                       color_variable = "condition_tp", 
                       shape_variable = "Cell_line_label_and_exp_id",
                       ntop_genes = 1000)+
  ggtitle("Batch Corrected Dataset") +
  scale_shape_manual(values = c(0,1,2,15,16,17)) +
  scale_color_manual(values = c("#DD3344", "#FF9F1C" ,"#553388"))
pca_deg_NObatch_experiment

ggsave(filename = paste0(deg_dir,"publication/", "TpNK_tp2_vs_Tonly_tp1_AB_PCA_not_batch_corrected.png"), 
       plot=pca_deg,
       width = 20, height = 14, units = "cm")

ggsave(filename = paste0(deg_dir,"publication/", "TpNK_tp2_vs_Tonly_tp1_AB_PCA_batch_corrected.png"), 
       plot=pca_deg_NObatch_experiment,
       width = 20, height = 14, units = "cm")

ggsave(filename = paste0(deg_dir,"publication/", "TpNK_tp2_vs_Tonly_tp1_AB_PCA_not_batch_corrected.eps"), 
       plot=pca_deg,
       width = 20, height = 14, units = "cm")

ggsave(filename = paste0(deg_dir,"publication/", "TpNK_tp2_vs_Tonly_tp1_AB_PCA_batch_corrected.eps"), 
       plot=pca_deg_NObatch_experiment,
       width = 20, height = 14, units = "cm")

The following PCA plots show effect of removing batch effects (experiment). There are two weird samples from EXP9.7 that create a lot of variance. What should i do with them?

print(pca_deg_NObatch_experiment)

print(pca_deg)

#resultsNames(dds_subset_filt)
# timepoint2 (T+NK)_vs_timepoint1 (T)
deg_TpNK_tp2_vs_Tonly_tp1_results <- generateResults_upd(dds_object = dds_subset_filt, 
                                                             coeff_name = "condition_tp_Tumor_plus_NK_timepoint_2_vs_Tumor_only_timepoint_1",
                                                             cond_numerator = "Tumor_plus_NK_timepoint_2", 
                                                             cond_denominator = "Tumor_only_timepoint_1",
                                                             cond_variable="condition_tp")

openxlsx::write.xlsx(deg_TpNK_tp2_vs_Tonly_tp1_results, file = file.path(deg_dir, "deg_TpNK_tp2_vs_Tonly_tp1_AB_results.xlsx"))

AB_results <- deg_TpNK_tp2_vs_Tonly_tp1_results
# Loading MsigDB geneset collections ----
gs_hallmark <- hypeR::msigdb_gsets(species = "Mus musculus", category = c("H"), clean=TRUE) 
gs_C2_kegg <- hypeR::msigdb_gsets(species = "Mus musculus", category = c("C2"), subcategory = "CP:KEGG", clean=TRUE) 
gs_C5_GOBP <- hypeR::msigdb_gsets(species = "Mus musculus", category = c("C5"), subcategory = "GO:BP", clean=TRUE) 
gs_C5_GOCC <- hypeR::msigdb_gsets(species = "Mus musculus", category = c("C5"), subcategory = "GO:CC", clean=TRUE) 
gs_C5_GOMF <- hypeR::msigdb_gsets(species = "Mus musculus", category = c("C5"), subcategory = "GO:MF", clean=TRUE) 


TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOBP <- hypeR::hypeR(signature = deg_TpNK_tp2_vs_Tonly_tp1_results$results_signif$mgi_symbol, 
                                                      genesets = gs_C5_GOBP, 
                                                      test="hypergeometric", 
                                                      background=nrow(dds_subset_filt))
AB_GOBP <- TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOBP

TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOCC <- hypeR::hypeR(signature = deg_TpNK_tp2_vs_Tonly_tp1_results$results_signif$mgi_symbol, 
                                                      genesets = gs_C5_GOCC, 
                                                      test="hypergeometric", 
                                                      background=nrow(dds_subset_filt))
AB_GOCC <- TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOCC

TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOMF <- hypeR::hypeR(signature = deg_TpNK_tp2_vs_Tonly_tp1_results$results_signif$mgi_symbol, 
                                                      genesets = gs_C5_GOMF, 
                                                      test="hypergeometric", 
                                                      background=nrow(dds_subset_filt))
AB_GOMF <- TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOMF


#hypeR::hyp_show(hyp_obj$data$clA, simple = FALSE)
hypeR::hyp_to_excel(TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOBP, file_path=file.path(deg_dir, "TpNK_tp2_vs_Tonly_tp1_AB_enrichment_C5_GOBP.xlsx"))
hypeR::hyp_to_excel(TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOCC, file_path=file.path(deg_dir, "TpNK_tp2_vs_Tonly_tp1_AB_enrichment_C5_GOCC.xlsx"))
hypeR::hyp_to_excel(TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOMF, file_path=file.path(deg_dir, "TpNK_tp2_vs_Tonly_tp1_AB_enrichment_C5_GOMF.xlsx"))

# plotting enrichment results ----
TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOBP_plot <- hypeR::hyp_dots(TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOBP, merge=TRUE, fdr=0.05, top = 20, abrv=70, val="fdr", title="GOBP: T+NK timepoint2 vs T-only timepoint1 AB") +theme_bw()
TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOCC_plot <- hypeR::hyp_dots(TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOCC, merge=TRUE, fdr=0.05, top = 20, abrv=70, val="fdr", title="GOCC: T+NK timepoint2 vs T-only timepoint1 AB") +theme_bw()
TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOMF_plot <- hypeR::hyp_dots(TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOMF, merge=TRUE, fdr=0.05, top = 20, abrv=70, val="fdr", title="GOMF:T+NK timepoint2 vs T-only timepoint1 AB") + theme_bw()


###### For publication ######
ggsave(filename = paste0(deg_dir,"publication/" ,"TpNK_tp2_vs_Tonly_tp1_AB_hyp_GOBP_plot.png"), 
       plot=TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOBP_plot,
       width = 20, height = 20, units = "cm")
ggsave(filename = paste0(deg_dir,"publication/" ,"TpNK_tp2_vs_Tonly_tp1_AB_hyp_GOBP_plot.eps"), 
       plot=TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOBP_plot,
       width = 20, height = 20, units = "cm")

Highlighting genes of interest

Volcano plot highlighting genes of interest:

# volcano plot
# heatmap - corrected
genes_of_interest
## $Denn2b
## [1] "ENSMUSG00000031024"
## 
## $Gbp6
## [1] "ENSMUSG00000104713"
## 
## $`H2-K1`
## [1] "ENSMUSG00000061232"
## 
## $Hs3st2
## [1] "ENSMUSG00000046321"
## 
## $Htra3
## [1] "ENSMUSG00000029096"
## 
## $Ifi27
## [1] "ENSMUSG00000064215"
## 
## $Igtp
## [1] "ENSMUSG00000078853"
## 
## $Irf1
## [1] "ENSMUSG00000018899"
## 
## $Lgals3bp
## [1] "ENSMUSG00000033880"
## 
## $Ly6a
## [1] "ENSMUSG00000075602"
## 
## $Plaat3
## [1] "ENSMUSG00000060675"
## 
## $Rtp4
## [1] "ENSMUSG00000033355"
## 
## $Serpina3f
## [1] "ENSMUSG00000066363"
## 
## $Stat1
## [1] "ENSMUSG00000026104"
genes_of_interest_exp9.4_9.7
## $Ifi44
## [1] "ENSMUSG00000028037"
## 
## $Ccl5
## [1] "ENSMUSG00000035042"
## 
## $Iigp1
## [1] "ENSMUSG00000054072"
## 
## $Serpina3f
## [1] "ENSMUSG00000066363"
## 
## $Ly6a
## [1] "ENSMUSG00000075602"
## 
## $Plaat3
## [1] "ENSMUSG00000060675"
## 
## $Lgals3bp
## [1] "ENSMUSG00000033880"
## 
## $Ifi27
## [1] "ENSMUSG00000064215"
## 
## $Gbp6
## [1] "ENSMUSG00000104713"
## 
## $Rtp4
## [1] "ENSMUSG00000033355"
## 
## $Stat1
## [1] "ENSMUSG00000026104"
## 
## $Tap1
## [1] "ENSMUSG00000037321"
## 
## $`H2-K1`
## [1] "ENSMUSG00000061232"
## 
## $B2m
## [1] "ENSMUSG00000060802"
## 
## $Igtp
## [1] "ENSMUSG00000078853"
## 
## $Gtf2ird1
## [1] "ENSMUSG00000023079"
## 
## $Cdc42ep4
## [1] "ENSMUSG00000041598"
## 
## $Hoxb6
## [1] "ENSMUSG00000000690"
## 
## $Bfsp2
## [1] "ENSMUSG00000032556"
## 
## $Sox6
## [1] "ENSMUSG00000051910"
## 
## $Tgm2
## [1] "ENSMUSG00000037820"
## 
## $Adprhl1
## [1] "ENSMUSG00000031448"
TpNK_tp2_vs_Tonly_tp1_volcano_plot_1 <- plotVolcano(dds_results_obj = deg_TpNK_tp2_vs_Tonly_tp1_results$results_all, 
                                             genes_of_interest = names(genes_of_interest), 
                                             plot_title = "T+NK tp2 vs T-only tp1 AB genes set 1")

TpNK_tp2_vs_Tonly_tp1_volcano_plot_2 <- plotVolcano(dds_results_obj = deg_TpNK_tp2_vs_Tonly_tp1_results$results_all, 
                                             genes_of_interest = names(genes_of_interest_exp9.4_9.7), 
                                             plot_title = "T+NK tp2 vs T-only tp1 AB genes set 2")


##### For publication #####

TpNK_tp2_vs_Tonly_tp1_volcano_plot_2 <- plotVolcano_repel(dds_results_obj = deg_TpNK_tp2_vs_Tonly_tp1_results$results_all, 
                                             genes_of_interest = names(genes_of_interest_exp9.4_9.7), 
                                             plot_title = "T+NK tp2 vs T-only tp1 AB")

ggsave(filename = paste0(deg_dir,"publication/", "TpNK_tp2_vs_Tonly_tp1_AB_volcano_plot.png"), 
       plot=TpNK_tp2_vs_Tonly_tp1_volcano_plot_2,
       width = 18, height = 12, units = "cm")
ggsave(filename = paste0(deg_dir,"publication/", "TpNK_tp2_vs_Tonly_tp1_AB_volcano_plot.eps"), 
       plot=TpNK_tp2_vs_Tonly_tp1_volcano_plot_2,
       width = 18, height = 12, units = "cm")
print(TpNK_tp2_vs_Tonly_tp1_volcano_plot_1)

print(TpNK_tp2_vs_Tonly_tp1_volcano_plot_2)

Heatmap of normalized and scaled expression of 607 significantly differentially expressed genes before and after removeal of batch effects coming from different experiments:

# heatmap and heatmap batch corrected - corrected 
metadata_heatmap <- as.data.frame(colData(dds_subset))

heatmap_counts <- SummarizedExperiment::assay(transf_object)
heatmap_counts_NObatch <- SummarizedExperiment::assay(transf_batch_NObatch_experiment) # removed xperiment batch effects! ? use experimen+cell_line removed???

heatmap_counts_deg <- heatmap_counts[rownames(heatmap_counts) %in% deg_TpNK_tp2_vs_Tonly_tp1_results$results_signif$ensembl_id, ]
heatmap_counts_NObatch_deg <- heatmap_counts_NObatch[rownames(heatmap_counts_NObatch) %in% deg_TpNK_tp2_vs_Tonly_tp1_results$results_signif$ensembl_id, ]

annotation_col <- metadata_heatmap %>%
  dplyr::select(experiment, cell_line_label, condition_tp) %>% # condition, timepoint_cell_harvesting, 
  dplyr::arrange(condition_tp, cell_line_label)

heatmap_counts_deg_ord <- heatmap_counts_deg[, match(rownames(annotation_col), colnames(heatmap_counts_deg))]
heatmap_counts_NObatch_deg_ord <- heatmap_counts_NObatch_deg[, match(rownames(annotation_col), colnames(heatmap_counts_NObatch_deg))]

ensembl2symbol_annot <- deg_TpNK_tp2_vs_Tonly_tp1_results$results_all %>%
  dplyr::select(ensembl_id, mgi_symbol)

color.scheme <- rev(RColorBrewer::brewer.pal(8,"RdBu")) # generate the color scheme to use

ann_colors = list(
  experiment = c( EXP9.5 = "#005f73", EXP9.6 = "#0a9396", EXP9.7 = "#94d2bd"),
  condition_tp = c(Tumor_only_timepoint_1 = "#DD3344", Tumor_plus_NK_timepoint_1 = "#FF9F1C",  Tumor_plus_NK_timepoint_2 = "#553388"),
  cell_line_label = c(A = "#E9D8A6", B = "#D9BE6D")
)

heatmap_not_corrected <- pheatmap::pheatmap(heatmap_counts_deg_ord,
                   main = "Heatmap of signif. DEG",
                   scale = "row",
                   annotation_col = annotation_col,
                   annotation_colors = ann_colors,
                   #annotation_row = row_annot_symbols,
                   show_colnames = FALSE,
                   show_rownames = FALSE,
                   cluster_cols = FALSE,
                   #cluster_rows = counts_deg_ord_row_cor_hclust,
                   color = color.scheme,
                   fontsize = 10, fontsize_row = 10 #height=10, cellwidth = 11, cellheight = 11
                   ) # 

ggsave(filename = paste0(deg_dir, "TpNK_tp2_vs_Tonly_tp1_AB_heatmap_not_corrected.png"), 
       plot=heatmap_not_corrected,
       width = 20, height = 20, units = "cm")
# heatmap and heatmap batch corrected - corrected 
metadata_heatmap <- as.data.frame(colData(dds_subset))

heatmap_counts <- SummarizedExperiment::assay(transf_object)
heatmap_counts_NObatch <- SummarizedExperiment::assay(transf_batch_NObatch_experiment) # removed xperiment batch effects! ? use experimen+cell_line removed???

heatmap_counts_deg <- heatmap_counts[rownames(heatmap_counts) %in% deg_TpNK_tp2_vs_Tonly_tp1_results$results_signif$ensembl_id, ]
heatmap_counts_NObatch_deg <- heatmap_counts_NObatch[rownames(heatmap_counts_NObatch) %in% deg_TpNK_tp2_vs_Tonly_tp1_results$results_signif$ensembl_id, ]

annotation_col <- metadata_heatmap %>%
  dplyr::select(experiment, cell_line_label, condition_tp) %>% # condition, timepoint_cell_harvesting, 
  dplyr::arrange(condition_tp, cell_line_label)

heatmap_counts_deg_ord <- heatmap_counts_deg[, match(rownames(annotation_col), colnames(heatmap_counts_deg))]
heatmap_counts_NObatch_deg_ord <- heatmap_counts_NObatch_deg[, match(rownames(annotation_col), colnames(heatmap_counts_NObatch_deg))]

ensembl2symbol_annot <- deg_TpNK_tp2_vs_Tonly_tp1_results$results_all %>%
  dplyr::select(ensembl_id, mgi_symbol)

color.scheme <- rev(RColorBrewer::brewer.pal(8,"RdBu")) # generate the color scheme to use

ann_colors = list(
  experiment = c( EXP9.5 = "#005f73", EXP9.6 = "#0a9396", EXP9.7 = "#94d2bd"),
  condition_tp = c(Tumor_only_timepoint_1 = "#DD3344", Tumor_plus_NK_timepoint_1 = "#FF9F1C",  Tumor_plus_NK_timepoint_2 = "#553388"),
  cell_line_label = c(A = "#E9D8A6", B = "#D9BE6D")
)

heatmap_corrected <- pheatmap::pheatmap(heatmap_counts_NObatch_deg_ord,
                   main = "Heatmap of signif. DEG - Batch Corrected",
                   scale = "row",
                   annotation_col = annotation_col,
                   annotation_colors = ann_colors,
                   #annotation_row = row_annot_symbols,
                   show_colnames = FALSE,
                   show_rownames = FALSE,
                   cluster_cols = FALSE,
                   #cluster_rows = counts_deg_ord_row_cor_hclust,
                   color = color.scheme,
                   fontsize = 10, fontsize_row = 10 #height=10, cellwidth = 11, cellheight = 11
                   ) # 

ggsave(filename = paste0(deg_dir, "TpNK_tp2_vs_Tonly_tp1_AB_heatmap_corrected.png"), 
       plot=heatmap_corrected,
       width = 20, height = 20, units = "cm")


##### For publication #####
ggsave(filename = paste0(deg_dir, "publication/", "TpNK_tp2_vs_Tonly_tp1_AB_heatmap_corrected.eps"), 
       plot=heatmap_corrected,
       width = 18, height = 20, units = "cm")
ggsave(filename = paste0(deg_dir, "publication/", "TpNK_tp2_vs_Tonly_tp1_AB_heatmap_corrected.png"), 
       plot=heatmap_corrected,
       width = 18, height = 20, units = "cm")
# identify the 25 gene set
minL2FC_Ids <- order(deg_TpNK_tp2_vs_Tonly_tp1_results$results_signif$log2FoldChange)[1:25]
maxL2FC_Ids <- order(decreasing = T, deg_TpNK_tp2_vs_Tonly_tp1_results$results_signif$log2FoldChange)[1:25]
L2FC_EMSEMBL <- deg_TpNK_tp2_vs_Tonly_tp1_results$results_signif$ensembl_id[c(minL2FC_Ids, maxL2FC_Ids)]
L2FC_EMSEMBL <- c(L2FC_EMSEMBL, "ENSMUSG00000075602", "ENSMUSG00000060675")

heatmap_counts_NObatch <- SummarizedExperiment::assay(transf_batch_NObatch_experiment) # removed xperiment batch effects! ? use experimen+cell_line removed???

heatmap_counts_NObatch_deg <- heatmap_counts_NObatch[rownames(heatmap_counts_NObatch) %in% L2FC_EMSEMBL, ]

annotation_col <- metadata_heatmap %>%
  dplyr::select(experiment, cell_line_label, condition_tp) %>% # condition, timepoint_cell_harvesting, 
  dplyr::arrange(condition_tp, cell_line_label)

heatmap_counts_NObatch_deg_ord <- heatmap_counts_NObatch_deg[, match(rownames(annotation_col), colnames(heatmap_counts_NObatch_deg))]

ensembl2symbol_annot <- deg_TpNK_tp2_vs_Tonly_tp1_results$results_all %>%
  dplyr::select(ensembl_id, mgi_symbol)

rownames(heatmap_counts_NObatch_deg_ord) <- ensembl2symbol_annot[match(x = row.names(heatmap_counts_NObatch_deg_ord), table = ensembl2symbol_annot$ensembl_id),]$mgi_symbol

color.scheme <- rev(RColorBrewer::brewer.pal(8,"RdBu")) # generate the color scheme to use

ann_colors = list(
  experiment = c( EXP9.5 = "#005f73", EXP9.6 = "#0a9396", EXP9.7 = "#94d2bd"),
  condition_tp = c(Tumor_only_timepoint_1 = "#DD3344", Tumor_plus_NK_timepoint_1 = "#FF9F1C",  Tumor_plus_NK_timepoint_2 = "#553388"),
  cell_line_label = c(A = "#E9D8A6", B = "#D9BE6D")
)


heatmap_corrected <- pheatmap::pheatmap(heatmap_counts_NObatch_deg_ord,
                   main = "Heatmap of signif. DEG - Batch Corrected 50 most regulated genes",
                   scale = "row",
                   annotation_col = annotation_col,
                   annotation_colors = ann_colors,
                   #annotation_row = row_annot_symbols,
                   show_colnames = FALSE,
                   show_rownames = T,
                   cluster_cols = FALSE,
                   #cluster_rows = counts_deg_ord_row_cor_hclust,
                   color = color.scheme,
                   fontsize = 10, fontsize_row = 10 #height=10, cellwidth = 11, cellheight = 11
                   ) # 

<img src=“/home/rstudio/workspace/nk_tum_immunoediting_files/figure-html/03 AB”small heatmap”-1.png” width=“672” />

##### For publication #####
ggsave(filename = paste0(deg_dir, "publication/", "TpNK_tp2_vs_Tonly_tp1_AB_heatmap_corrected_50genes.eps"), 
       plot=heatmap_corrected,
       width = 24, height = 20, units = "cm")
ggsave(filename = paste0(deg_dir, "publication/", "TpNK_tp2_vs_Tonly_tp1_AB_heatmap_corrected_50_genes.png"), 
       plot=heatmap_corrected,
       width = 24, height = 20, units = "cm")

Now - process the CD subset. Later I will check the overlap between these two.

# preparing dds and vsd objects for the main project comparison 
# We use only EXP 9.4 - 9.7. EXP 9.8 is excluded.
# Preparing CD subset. 
# defining parameters

deg_name <- "TpNK_tp2_vs_Tonly_tp1_CD"
deg_dir <- file.path(output_dir, paste0("deg_", deg_name, "/"))
dir.create(deg_dir)

precomputed_objects_deg_filename <- paste0(deg_name, "_dds_objects.RData")
precomputed_objects_deg_file <- file.path(deg_dir, precomputed_objects_deg_filename)

precomputed_transf_objects_deg_filename <- paste0(deg_name, "_log2_vsd_filt_objects.RData")
precomputed_transf_objects_deg_file <- file.path(deg_dir, precomputed_transf_objects_deg_filename)

experiment_subset <- c("EXP9.4", "EXP9.5", "EXP9.6", "EXP9.7")


# just in case remove any existing subsets
if(exists("dds_subset")) {rm(dds_subset)}
if(exists("dds_subset_filt")) {rm(dds_subset_filt)}
if(exists("vsd_subset")) {rm(vsd_subset)}

if(!file.exists(precomputed_objects_deg_file)){
  cat("RNA objects file '", precomputed_objects_deg_filename, "' does not exist in the OUTPUT_DIR...\n")
  cat("generating", precomputed_objects_deg_filename, "\n")

  dds_subset <- dds_filt[ , dds_filt$condition_tp %in% condition_tp_subset]  
  dds_subset <- dds_subset[ , dds_subset$experiment %in% experiment_subset]
  dds_subset <- dds_subset[ , dds_subset$cell_line_label %in% c("C", "D")]
  
  # fixing Tumor_plus_WT_NK_timepoint_2 -> Tumor_plus_NK_timepoint_2
  dds_subset$condition_tp <- droplevels(dds_subset$condition_tp)
  table(dds_subset$condition_tp)
  
  dds_subset$experiment <- droplevels(dds_subset$experiment)
  table(dds_subset$experiment)
  
  dds_subset$cell_line_label <- droplevels(dds_subset$cell_line_label)
  table(dds_subset$cell_line_label)
  
  # filtering lowly expressed genes
 
  dds_subset_filt <- filterDatasets(dds_subset, 
                                    abs_filt = TRUE, 
                                    abs_filt_samples = abs_filt_samples) # at least in N samples, which is a smallest group size

  cat("... dds\n")
  design(dds_subset_filt) <- deg_design
  dds_subset_filt <- DESeq2::estimateSizeFactors(dds_subset_filt)
  dds_subset_filt <- DESeq2::DESeq(dds_subset_filt) # do not replace outliers based on replicates
  
  cat("saving...", precomputed_objects_file, "\n")
  cat("...into:", output_dir, "\n")
  save(dds_subset, dds_subset_filt, ensemblAnnot,  file = precomputed_objects_deg_file)
  
} else{
  cat("RNA objects file '", precomputed_objects_deg_filename, "' exist...loading\n")
  load(precomputed_objects_deg_file)
}
## RNA objects file ' TpNK_tp2_vs_Tonly_tp1_CD_dds_objects.RData ' exist...loading
#transformation after filtering
if(!file.exists(precomputed_transf_objects_deg_file)){
  # transformation
  log2_norm_subset_filt <- DESeq2::normTransform(dds_subset_filt)
  vsd_subset_filt <- DESeq2::vst(dds_subset_filt, blind = FALSE) # using blind=FALSE utilize design info; blind = TRUE for QC
  #rld_filt <- DESeq2::rlog(dds_subset_filt, blind = FALSE) # more robust to differences in sequencing depth!
  #rld_filt - too big for >100 samples; skipping!
  #rld_filt <- rlog(dds_filt, blind = FALSE) # not blind to batch effects
  save(log2_norm_subset_filt, vsd_subset_filt,   
       file = precomputed_transf_objects_deg_file)
} else{
  load(precomputed_transf_objects_deg_file)
}
#print(key_variables_tableOne$CatTable)
rm(key_metadata_subset, key_variables_tableOne) # in case this exists from previus runs
key_metadata_subset <- as.data.frame(colData(dds_subset)) %>%
  dplyr::select(experiment, condition_tp, cell_line_label, technical_replicate) 

key_variables_tableOne <- tableone::CreateTableOne(vars = colnames(dplyr::select(key_metadata_subset, -condition_tp)), 
                           strata = c("condition_tp"), 
                           data = key_metadata_subset)

tableone::kableone(key_variables_tableOne$CatTable,
                   caption = "Overview of number of samples in different categories (experiment, condition,...).") %>%
  kableExtra::kable_material(c("striped", "hover"))
Overview of number of samples in different categories (experiment, condition,…).
Tumor_only_timepoint_1 Tumor_plus_NK_timepoint_1 Tumor_plus_NK_timepoint_2 p test
n 24 24 23
experiment (%) 1.000
EXP9.4 6 (25.0) 6 (25.0) 6 (26.1)
EXP9.5 6 (25.0) 6 (25.0) 6 (26.1)
EXP9.6 6 (25.0) 6 (25.0) 5 (21.7)
EXP9.7 6 (25.0) 6 (25.0) 6 (26.1)
cell_line_label = D (%) 12 (50.0) 12 (50.0) 11 (47.8) 0.985
technical_replicate (%) 1.000
1 8 (33.3) 8 (33.3) 8 (34.8)
2 8 (33.3) 8 (33.3) 8 (34.8)
3 8 (33.3) 8 (33.3) 7 (30.4)
#TODO:
# - plot images after removing batch effects!
# EDA and visualize plots before and after batch effect correction
transf_object <- vsd_subset_filt
pca_deg <- generatePCA(transf_object = transf_object, 
                       cond_interest_varPart = c("condition_tp", "cell_line_label", "experiment"), 
                       color_variable = "condition_tp", 
                       shape_variable = "experiment",
                       ntop_genes = 1000) +
  ggtitle("Original dataset")

pca_deg_plotCellLabel <- generatePCA(transf_object = transf_object, 
                       cond_interest_varPart = c("condition_tp", "cell_line_label", "experiment"), 
                       color_variable = "condition_tp", 
                       shape_variable = "cell_line_label",
                       ntop_genes = 1000) +
  ggtitle("Original dataset")

# Remove batch effect
# remove batch effect
transf_batch_NObatch_experiment <- vsd_subset_filt
transf_batch_NObatch_experiment_count <- limma::removeBatchEffect(SummarizedExperiment::assay(transf_batch_NObatch_experiment), transf_batch_NObatch_experiment$experiment)
SummarizedExperiment::assay(transf_batch_NObatch_experiment) <- transf_batch_NObatch_experiment_count

pca_deg_NObatch_experiment <- generatePCA(transf_object = transf_batch_NObatch_experiment, 
                       cond_interest_varPart = c("condition_tp", "cell_line_label", "experiment"), 
                       color_variable = "condition_tp", 
                       shape_variable = "experiment",
                       ntop_genes = 1000) +
  ggtitle("Removed batchEffect - experiment")

pca_deg_NObatch_experiment_plotCellLabel <- generatePCA(transf_object = transf_batch_NObatch_experiment, 
                       cond_interest_varPart = c("condition_tp", "cell_line_label", "experiment"), 
                       color_variable = "condition_tp", 
                       shape_variable = "cell_line_label",
                       ntop_genes = 1000) +
  ggtitle("Removed batchEffect - experiment")

#PCA_batch_merged <- ggpubr::ggarrange(pca_deg, pca_deg_NObatch_experiment, common.legend = TRUE)


PCA_batch_merged <- ggpubr::ggarrange(pca_deg, 
                                      pca_deg_NObatch_experiment,
                                      common.legend = TRUE)

PCA_batch_highlightCellLines_merged <- ggpubr::ggarrange(pca_deg_plotCellLabel, 
                                      pca_deg_NObatch_experiment_plotCellLabel,
                                      common.legend = TRUE)


#### for publication #####
transf_object <- vsd_subset_filt
pca_deg <- generatePCA_plus_shape(transf_object = transf_object, 
                       cond_interest_varPart = c("cell_line_label", "experiment","condition_tp"), 
                       color_variable = "condition_tp", 
                       shape_variable = "Cell_line_label_and_exp_id",
                       ntop_genes = 1000)+
  ggtitle("Original dataset") +
  scale_shape_manual(values = c(0,1,2,5,15,16,17,18)) +
  scale_color_manual(values = c("#DD3344", "#FF9F1C", "#553388"))
pca_deg

# remove batch effect
transf_batch_NObatch_experiment <- vsd_subset_filt
transf_batch_NObatch_experiment_count <- limma::removeBatchEffect(SummarizedExperiment::assay(transf_batch_NObatch_experiment), transf_batch_NObatch_experiment$experiment)
SummarizedExperiment::assay(transf_batch_NObatch_experiment) <- transf_batch_NObatch_experiment_count


pca_deg_NObatch_experiment <- generatePCA_plus_shape(transf_object = transf_batch_NObatch_experiment, 
                       cond_interest_varPart = c("cell_line_label", "experiment","condition_tp"), 
                       color_variable = "condition_tp", 
                       shape_variable = "Cell_line_label_and_exp_id",
                       ntop_genes = 1000)+
  ggtitle("Batch Corrected Dataset") +
  scale_shape_manual(values = c(0,1,2,5,15,16,17,18)) +
  scale_color_manual(values = c("#DD3344", "#FF9F1C", "#553388"))
pca_deg_NObatch_experiment

ggsave(filename = paste0(deg_dir,"publication/", "TpNK_tp2_vs_Tonly_tp1_CD_PCA_not_batch_corrected.png"), 
       plot=pca_deg,
       width = 20, height = 14, units = "cm")

ggsave(filename = paste0(deg_dir,"publication/", "TpNK_tp2_vs_Tonly_tp1_CD_PCA_batch_corrected.png"), 
       plot=pca_deg_NObatch_experiment,
       width = 20, height = 14, units = "cm")

ggsave(filename = paste0(deg_dir,"publication/", "TpNK_tp2_vs_Tonly_tp1_CD_PCA_not_batch_corrected.eps"), 
       plot=pca_deg,
       width = 20, height = 14, units = "cm")

ggsave(filename = paste0(deg_dir,"publication/", "TpNK_tp2_vs_Tonly_tp1_CD_PCA_batch_corrected.eps"), 
       plot=pca_deg_NObatch_experiment,
       width = 20, height = 14, units = "cm")
#resultsNames(dds_subset_filt)

# timepoint2 (T+NK)_vs_timepoint1 (T)
deg_TpNK_tp2_vs_Tonly_tp1_results <- generateResults_upd(dds_object = dds_subset_filt, 
                                                             coeff_name = "condition_tp_Tumor_plus_NK_timepoint_2_vs_Tumor_only_timepoint_1",
                                                             cond_numerator = "Tumor_plus_NK_timepoint_2", 
                                                             cond_denominator = "Tumor_only_timepoint_1",
                                                             cond_variable="condition_tp")
CD_results <- deg_TpNK_tp2_vs_Tonly_tp1_results
openxlsx::write.xlsx(deg_TpNK_tp2_vs_Tonly_tp1_results, file = file.path(deg_dir, "deg_TpNK_tp2_vs_Tonly_tp1_CD_results.xlsx"))
#msigdbr::msigdbr_species()
#packageVersion("msigdbr")
#msigdbr::msigdbr_collections()

# Loading MsigDB geneset collections ----
gs_hallmark <- hypeR::msigdb_gsets(species = "Mus musculus", category = c("H"), clean=TRUE) 
gs_C2_kegg <- hypeR::msigdb_gsets(species = "Mus musculus", category = c("C2"), subcategory = "CP:KEGG", clean=TRUE) 
gs_C5_GOBP <- hypeR::msigdb_gsets(species = "Mus musculus", category = c("C5"), subcategory = "GO:BP", clean=TRUE) 
gs_C5_GOCC <- hypeR::msigdb_gsets(species = "Mus musculus", category = c("C5"), subcategory = "GO:CC", clean=TRUE) 
gs_C5_GOMF <- hypeR::msigdb_gsets(species = "Mus musculus", category = c("C5"), subcategory = "GO:MF", clean=TRUE) 

TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOBP <- hypeR::hypeR(signature = deg_TpNK_tp2_vs_Tonly_tp1_results$results_signif$mgi_symbol, 
                                                      genesets = gs_C5_GOBP, 
                                                      test="hypergeometric", 
                                                      background=nrow(dds_subset_filt))
CD_GOBP <- TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOBP

TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOCC <- hypeR::hypeR(signature = deg_TpNK_tp2_vs_Tonly_tp1_results$results_signif$mgi_symbol, 
                                                      genesets = gs_C5_GOCC, 
                                                      test="hypergeometric", 
                                                      background=nrow(dds_subset_filt))
CD_GOCC <- TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOCC

TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOMF <- hypeR::hypeR(signature = deg_TpNK_tp2_vs_Tonly_tp1_results$results_signif$mgi_symbol, 
                                                      genesets = gs_C5_GOMF, 
                                                      test="hypergeometric", 
                                                      background=nrow(dds_subset_filt))
CD_GOMF <- TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOMF

#hypeR::hyp_show(hyp_obj$data$clA, simple = FALSE)
hypeR::hyp_to_excel(TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOBP, file_path=file.path(deg_dir, "TpNK_tp2_vs_Tonly_tp1_CD_enrichment_C5_GOBP.xlsx"))
hypeR::hyp_to_excel(TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOCC, file_path=file.path(deg_dir, "TpNK_tp2_vs_Tonly_tp1_CD_enrichment_C5_GOCC.xlsx"))
hypeR::hyp_to_excel(TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOMF, file_path=file.path(deg_dir, "TpNK_tp2_vs_Tonly_tp1_CD_enrichment_C5_GOMF.xlsx"))

# plotting enrichment results ----
TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOBP_plot <- hypeR::hyp_dots(TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOBP, merge=TRUE, fdr=0.05, top = 20, abrv=70, val="fdr", title="GOBP: T+NK timepoint2 vs T-only timepoint1 CD")+theme_bw()

TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOCC_plot <- hypeR::hyp_dots(TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOCC, merge=TRUE, fdr=0.05, top = 20, abrv=70, val="fdr", title="GOCC: T+NK timepoint2 vs T-only timepoint1 CD")+theme_bw()

TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOMF_plot <- hypeR::hyp_dots(TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOMF, merge=TRUE, fdr=0.05, top = 20, abrv=70, val="fdr", title="GOMF:T+NK timepoint2 vs T-only timepoint1 CD")+theme_bw()

ggsave(filename = paste0(deg_dir, "TpNK_tp2_vs_Tonly_tp1_AB_hyp_GOBP_plot.png"), 
       plot=TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOBP_plot,
       width = 20, height = 20, units = "cm")
ggsave(filename = paste0(deg_dir, "TpNK_tp2_vs_Tonly_tp1_AB_hyp_GOCC_plot.png"), 
       plot=TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOCC_plot,
       width = 20, height = 20, units = "cm")
ggsave(filename = paste0(deg_dir, "TpNK_tp2_vs_Tonly_tp1_AB_hyp_GOMF_plot.png"), 
       plot=TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOMF_plot,
       width = 20, height = 20, units = "cm")


###### For publication ######

ggsave(filename = paste0(deg_dir, "publication/", "TpNK_tp2_vs_Tonly_tp1_AB_hyp_GOBP_plot.png"), 
       plot=TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOBP_plot,
       width = 20, height = 20, units = "cm")

ggsave(filename = paste0(deg_dir, "publication/", "TpNK_tp2_vs_Tonly_tp1_AB_hyp_GOBP_plot.eps"), 
       plot=TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOBP_plot,
       width = 20, height = 20, units = "cm")

Highlighting genes of interest

Volcano plot highlighting genes of interest:

# volcano plot
# heatmap - corrected
genes_of_interest
## $Denn2b
## [1] "ENSMUSG00000031024"
## 
## $Gbp6
## [1] "ENSMUSG00000104713"
## 
## $`H2-K1`
## [1] "ENSMUSG00000061232"
## 
## $Hs3st2
## [1] "ENSMUSG00000046321"
## 
## $Htra3
## [1] "ENSMUSG00000029096"
## 
## $Ifi27
## [1] "ENSMUSG00000064215"
## 
## $Igtp
## [1] "ENSMUSG00000078853"
## 
## $Irf1
## [1] "ENSMUSG00000018899"
## 
## $Lgals3bp
## [1] "ENSMUSG00000033880"
## 
## $Ly6a
## [1] "ENSMUSG00000075602"
## 
## $Plaat3
## [1] "ENSMUSG00000060675"
## 
## $Rtp4
## [1] "ENSMUSG00000033355"
## 
## $Serpina3f
## [1] "ENSMUSG00000066363"
## 
## $Stat1
## [1] "ENSMUSG00000026104"
genes_of_interest_exp9.4_9.7
## $Ifi44
## [1] "ENSMUSG00000028037"
## 
## $Ccl5
## [1] "ENSMUSG00000035042"
## 
## $Iigp1
## [1] "ENSMUSG00000054072"
## 
## $Serpina3f
## [1] "ENSMUSG00000066363"
## 
## $Ly6a
## [1] "ENSMUSG00000075602"
## 
## $Plaat3
## [1] "ENSMUSG00000060675"
## 
## $Lgals3bp
## [1] "ENSMUSG00000033880"
## 
## $Ifi27
## [1] "ENSMUSG00000064215"
## 
## $Gbp6
## [1] "ENSMUSG00000104713"
## 
## $Rtp4
## [1] "ENSMUSG00000033355"
## 
## $Stat1
## [1] "ENSMUSG00000026104"
## 
## $Tap1
## [1] "ENSMUSG00000037321"
## 
## $`H2-K1`
## [1] "ENSMUSG00000061232"
## 
## $B2m
## [1] "ENSMUSG00000060802"
## 
## $Igtp
## [1] "ENSMUSG00000078853"
## 
## $Gtf2ird1
## [1] "ENSMUSG00000023079"
## 
## $Cdc42ep4
## [1] "ENSMUSG00000041598"
## 
## $Hoxb6
## [1] "ENSMUSG00000000690"
## 
## $Bfsp2
## [1] "ENSMUSG00000032556"
## 
## $Sox6
## [1] "ENSMUSG00000051910"
## 
## $Tgm2
## [1] "ENSMUSG00000037820"
## 
## $Adprhl1
## [1] "ENSMUSG00000031448"
TpNK_tp2_vs_Tonly_tp1_volcano_plot_1 <- plotVolcano(dds_results_obj = deg_TpNK_tp2_vs_Tonly_tp1_results$results_all, 
                                             genes_of_interest = names(genes_of_interest), 
                                             plot_title = "T+NK tp2 vs T-only tp1 CD genes set 1")

TpNK_tp2_vs_Tonly_tp1_volcano_plot_2 <- plotVolcano(dds_results_obj = deg_TpNK_tp2_vs_Tonly_tp1_results$results_all, 
                                             genes_of_interest = names(genes_of_interest_exp9.4_9.7), 
                                             plot_title = "T+NK tp2 vs T-only tp1 CD genes set 2")


ggsave(filename = paste0(deg_dir, "TpNK_tp2_vs_Tonly_tp1_CD_volcano_plot_gene_set_1.png"), 
       plot=TpNK_tp2_vs_Tonly_tp1_volcano_plot_1,
       width = 20, height = 20, units = "cm")

ggsave(filename = paste0(deg_dir, "TpNK_tp2_vs_Tonly_tp1_CD_volcano_plot_gene_set_2.png"), 
       plot=TpNK_tp2_vs_Tonly_tp1_volcano_plot_2,
       width = 20, height = 20, units = "cm")


##### for publication ######

TpNK_tp2_vs_Tonly_tp1_volcano_plot_2 <- plotVolcano_repel(dds_results_obj = deg_TpNK_tp2_vs_Tonly_tp1_results$results_all, 
                                             genes_of_interest = names(genes_of_interest_exp9.4_9.7), 
                                             plot_title = "T+NK tp2 vs T-only tp1 CD")

ggsave(filename = paste0(deg_dir,"publication/", "TpNK_tp2_vs_Tonly_tp1_CD_volcano_plot.png"), 
       plot=TpNK_tp2_vs_Tonly_tp1_volcano_plot_2,
       width = 18, height = 12, units = "cm")
ggsave(filename = paste0(deg_dir,"publication/", "TpNK_tp2_vs_Tonly_tp1_CD_volcano_plot.eps"), 
       plot=TpNK_tp2_vs_Tonly_tp1_volcano_plot_2,
       width = 18, height = 12, units = "cm")
print(TpNK_tp2_vs_Tonly_tp1_volcano_plot_1)

print(TpNK_tp2_vs_Tonly_tp1_volcano_plot_2)

Heatmap of normalized and scaled expression of 321 significantly differentially expressed genes before and after removeal of batch effects coming from different experiments:

# heatmap and heatmap batch corrected - corrected 
metadata_heatmap <- as.data.frame(colData(dds_subset))

heatmap_counts <- SummarizedExperiment::assay(transf_object)
heatmap_counts_NObatch <- SummarizedExperiment::assay(transf_batch_NObatch_experiment) # removed xperiment batch effects! ? use experimen+cell_line removed???

heatmap_counts_NObatch_deg <- heatmap_counts_NObatch[rownames(heatmap_counts_NObatch) %in% deg_TpNK_tp2_vs_Tonly_tp1_results$results_signif$ensembl_id, ]

annotation_col <- metadata_heatmap %>%
  dplyr::select(experiment, cell_line_label, condition_tp) %>% # condition, timepoint_cell_harvesting, 
  dplyr::arrange(condition_tp, cell_line_label)

heatmap_counts_NObatch_deg_ord <- heatmap_counts_NObatch_deg[, match(rownames(annotation_col), colnames(heatmap_counts_NObatch_deg))]

ensembl2symbol_annot <- deg_TpNK_tp2_vs_Tonly_tp1_results$results_all %>%
  dplyr::select(ensembl_id, mgi_symbol)

color.scheme <- rev(RColorBrewer::brewer.pal(8,"RdBu")) # generate the color scheme to use

ann_colors = list(
  experiment = c(EXP9.4 = "#00262E", EXP9.5 = "#005f73", EXP9.6 = "#0a9396", EXP9.7 = "#94d2bd"),
  condition_tp = c(Tumor_only_timepoint_1 = "#DD3344", Tumor_plus_NK_timepoint_1 = "#FF9F1C", Tumor_plus_NK_timepoint_2 = "#553388"),
  cell_line_label = c(C = "#E9D8A6", D = "#D9BE6D")
)

heatmap_corrected <- pheatmap::pheatmap(heatmap_counts_NObatch_deg_ord,
                   main = "Heatmap of signif. DEG",
                   scale = "row",
                   annotation_col = annotation_col,
                   annotation_colors = ann_colors,
                   #annotation_row = row_annot_symbols,
                   show_colnames = FALSE,
                   show_rownames = FALSE,
                   cluster_cols = FALSE,
                   #cluster_rows = counts_deg_ord_row_cor_hclust,
                   color = color.scheme,
                   fontsize = 10, fontsize_row = 10 #height=10, cellwidth = 11, cellheight = 11
                   ) # 

ggsave(filename = paste0(deg_dir, "TpNK_tp2_vs_Tonly_tp1_CD_heatmap_batch_corrected.png"), 
       plot=heatmap_corrected,
       width = 20, height = 20, units = "cm")

###### For publication #####

ggsave(filename = paste0(deg_dir, "publication/", "TpNK_tp2_vs_Tonly_tp1_CD_heatmap_bathc_corrected.png"), 
       plot=heatmap_corrected,
       width = 20, height = 20, units = "cm")
ggsave(filename = paste0(deg_dir, "publication/", "TpNK_tp2_vs_Tonly_tp1_CD_heatmap_batch_corrected.eps"), 
       plot=heatmap_corrected,
       width = 20, height = 20, units = "cm")
# identify the 25 gene set
minL2FC_Ids <- order(deg_TpNK_tp2_vs_Tonly_tp1_results$results_signif$log2FoldChange)[1:25]
maxL2FC_Ids <- order(decreasing = T, deg_TpNK_tp2_vs_Tonly_tp1_results$results_signif$log2FoldChange)[1:25]
L2FC_EMSEMBL <- deg_TpNK_tp2_vs_Tonly_tp1_results$results_signif$ensembl_id[c(minL2FC_Ids, maxL2FC_Ids)]
L2FC_EMSEMBL <- c(L2FC_EMSEMBL, "ENSMUSG00000075602", "ENSMUSG00000060675")

heatmap_counts_NObatch <- SummarizedExperiment::assay(transf_batch_NObatch_experiment) # removed xperiment batch effects! ? use experimen+cell_line removed???

heatmap_counts_NObatch_deg <- heatmap_counts_NObatch[rownames(heatmap_counts_NObatch) %in% L2FC_EMSEMBL, ]

annotation_col <- metadata_heatmap %>%
  dplyr::select(experiment, cell_line_label, condition_tp) %>% # condition, timepoint_cell_harvesting, 
  dplyr::arrange(condition_tp, cell_line_label)

heatmap_counts_NObatch_deg_ord <- heatmap_counts_NObatch_deg[, match(rownames(annotation_col), colnames(heatmap_counts_NObatch_deg))]

ensembl2symbol_annot <- deg_TpNK_tp2_vs_Tonly_tp1_results$results_all %>%
  dplyr::select(ensembl_id, mgi_symbol)

rownames(heatmap_counts_NObatch_deg_ord) <- ensembl2symbol_annot[match(x = row.names(heatmap_counts_NObatch_deg_ord), table = ensembl2symbol_annot$ensembl_id),]$mgi_symbol

color.scheme <- rev(RColorBrewer::brewer.pal(8,"RdBu")) # generate the color scheme to use

ann_colors = list(
  experiment = c(EXP9.4 = "#00262E", EXP9.5 = "#005f73", EXP9.6 = "#0a9396", EXP9.7 = "#94d2bd"),
  condition_tp = c(Tumor_only_timepoint_1 = "#DD3344", Tumor_plus_NK_timepoint_1 = "#FF9F1C", Tumor_plus_NK_timepoint_2 = "#553388"),
  cell_line_label = c(C = "#E9D8A6", D = "#D9BE6D")
)


heatmap_corrected <- pheatmap::pheatmap(heatmap_counts_NObatch_deg_ord,
                   main = "Heatmap of signif. DEG - Batch Corrected 50 most regulated genes",
                   scale = "row",
                   annotation_col = annotation_col,
                   annotation_colors = ann_colors,
                   #annotation_row = row_annot_symbols,
                   show_colnames = FALSE,
                   show_rownames = T,
                   cluster_cols = FALSE,
                   #cluster_rows = counts_deg_ord_row_cor_hclust,
                   color = color.scheme,
                   fontsize = 10, fontsize_row = 10 #height=10, cellwidth = 11, cellheight = 11
                   ) # 

<img src=“/home/rstudio/workspace/nk_tum_immunoediting_files/figure-html/03 CD”small heatmap”-1.png” width=“672” />

##### For publication #####
ggsave(filename = paste0(deg_dir, "publication/", "TpNK_tp2_vs_Tonly_tp1_CD_heatmap_corrected_50_genes.eps"), 
       plot=heatmap_corrected,
       width = 24, height = 20, units = "cm")
ggsave(filename = paste0(deg_dir, "publication/", "TpNK_tp2_vs_Tonly_tp1_CD_heatmap_corrected_50_genes.png"), 
       plot=heatmap_corrected,
       width = 24, height = 20, units = "cm")
# heatmap and heatmap batch corrected - corrected 
metadata_heatmap <- as.data.frame(colData(dds_subset))

heatmap_counts <- SummarizedExperiment::assay(transf_object)
heatmap_counts_NObatch <- SummarizedExperiment::assay(transf_batch_NObatch_experiment) # removed xperiment batch effects! ? use experimen+cell_line removed???

heatmap_counts_deg <- heatmap_counts[rownames(heatmap_counts) %in% deg_TpNK_tp2_vs_Tonly_tp1_results$results_signif$ensembl_id, ]
heatmap_counts_NObatch_deg <- heatmap_counts_NObatch[rownames(heatmap_counts_NObatch) %in% deg_TpNK_tp2_vs_Tonly_tp1_results$results_signif$ensembl_id, ]

annotation_col <- metadata_heatmap %>%
  dplyr::select(experiment, cell_line_label, condition_tp) %>% # condition, timepoint_cell_harvesting, 
  dplyr::arrange(condition_tp, cell_line_label)

heatmap_counts_deg_ord <- heatmap_counts_deg[, match(rownames(annotation_col), colnames(heatmap_counts_deg))]
heatmap_counts_NObatch_deg_ord <- heatmap_counts_NObatch_deg[, match(rownames(annotation_col), colnames(heatmap_counts_NObatch_deg))]

ensembl2symbol_annot <- deg_TpNK_tp2_vs_Tonly_tp1_results$results_all %>%
  dplyr::select(ensembl_id, mgi_symbol)

color.scheme <- rev(RColorBrewer::brewer.pal(8,"RdBu")) # generate the color scheme to use

ann_colors = list(
  experiment = c(EXP9.4 = "#4682B4", EXP9.5 = "#7846B4", EXP9.6 = "#B47846", EXP9.7 = "#82B446", EXP9.8 = "grey")
)

heatmap_corrected <- pheatmap::pheatmap(heatmap_counts_NObatch_deg_ord,
                   main = "Heatmap of signif. DEG - removed experiment BatchEffect",
                   scale = "row",
                   annotation_col = annotation_col,
                   annotation_colors = ann_colors,
                   #annotation_row = row_annot_symbols,
                   show_colnames = FALSE,
                   show_rownames = FALSE,
                   cluster_cols = FALSE,
                   #cluster_rows = counts_deg_ord_row_cor_hclust,
                   color = color.scheme,
                   fontsize = 10, fontsize_row = 10 #height=10, cellwidth = 11, cellheight = 11
                   ) # 

ggsave(filename = paste0(deg_dir, "TpNK_tp2_vs_Tonly_tp1_CD_heatmap_corrected.png"), 
       plot=heatmap_corrected,
       width = 20, height = 20, units = "cm")
AB_CD_UP_venn <- list("AB_up" = AB_results$results_signif[AB_results$results_signif$log2FoldChange > 0,]$mgi_symbol,
                   "CD_up" = CD_results$results_signif[CD_results$results_signif$log2FoldChange > 0,]$mgi_symbol)

AB_CD_DOWN_venn <- list("AB_down" = AB_results$results_signif[AB_results$results_signif$log2FoldChange < 0,]$mgi_symbol,
                   "CD_down" = CD_results$results_signif[CD_results$results_signif$log2FoldChange < 0,]$mgi_symbol)

library(ggvenn)
AB_CD_UP_venn_plot <- ggvenn(
  AB_CD_UP_venn, 
  fill_color = c("#E0E2E3", "#8EA7CE"),
  stroke_size = 0.5, set_name_size = 4
  )


AB_CD_DOWN_venn_plot <- ggvenn(
  AB_CD_DOWN_venn, 
  fill_color = c("#E0E2E3", "#8EA7CE"),
  stroke_size = 0.5, set_name_size = 4
  )


deg_name <- "TpNK_tp2_AB_CD_comparison"
deg_dir <- file.path(output_dir, paste0("deg_", deg_name, "/"))

intersect(AB_results$results_signif[AB_results$results_signif$log2FoldChange < 0,]$mgi_symbol,
          CD_results$results_signif[CD_results$results_signif$log2FoldChange < 0,]$mgi_symbol)
##  [1] "Bfsp2"    "Hspb8"    "Ckap4"    "Denn2b"   "Hs3st2"   "Mgll"     "Cdc42ep4" "Egfl7"   
##  [9] "Akr1c13"  "Apbb1"    "Pygl"     "Tcf7l1"   "Gria2"    "Tmem176a" "Golm1"    "Ntrk3"   
## [17] "Tmem255a" "Tmem38b"  "Gm36120"  "Arid5b"   "n-R5s118" "H2ac15"   "Ramp2"    "Tmed6"   
## [25] "Rgs7bp"   "Ccdc120"  "Slc47a1"
ggsave(filename = paste0(deg_dir, "AB_CD_UP_venn_plot.png"), 
       plot=AB_CD_UP_venn_plot,
       width = 20, height = 20, units = "cm")

ggsave(filename = paste0(deg_dir, "AB_CD_UP_venn_plot.eps"), 
       plot=AB_CD_UP_venn_plot,
       width = 20, height = 20, units = "cm")

ggsave(filename = paste0(deg_dir, "AB_CD_DOWN_venn_plot.png"), 
       plot=AB_CD_DOWN_venn_plot,
       width = 20, height = 20, units = "cm")

ggsave(filename = paste0(deg_dir, "AB_CD_DOWN_venn_plot.eps"), 
       plot=AB_CD_DOWN_venn_plot,
       width = 20, height = 20, units = "cm")


#aggreagated excel sheet for 
Deg_AB_xlsx <- openxlsx::read.xlsx("~/workspace/results_dir/nk_tum_immunoedit_complete/deg_TpNK_tp2_vs_Tonly_tp1_AB/deg_TpNK_tp2_vs_Tonly_tp1_AB_results.xlsx",sheet = 'results_signif')
Deg_CD_xlsx <- openxlsx::read.xlsx("~/workspace/results_dir/nk_tum_immunoedit_complete/deg_TpNK_tp2_vs_Tonly_tp1_CD/deg_TpNK_tp2_vs_Tonly_tp1_CD_results.xlsx",sheet = 'results_signif')


AB_specific <- Deg_AB_xlsx[Deg_AB_xlsx$mgi_symbol %in% setdiff(Deg_AB_xlsx$mgi_symbol, Deg_CD_xlsx$mgi_symbol),]%>%
  select("ensembl_id", "mgi_symbol", "mgi_description", "log2FoldChange", "padj")
CD_specific <- Deg_CD_xlsx[Deg_CD_xlsx$mgi_symbol %in% setdiff(Deg_CD_xlsx$mgi_symbol, Deg_AB_xlsx$mgi_symbol),]%>%
  select("ensembl_id", "mgi_symbol", "mgi_description", "log2FoldChange", "padj")

AB_intersect_tmp <-  Deg_AB_xlsx[which(Deg_AB_xlsx$mgi_symbol %in% intersect(Deg_AB_xlsx$mgi_symbol, Deg_CD_xlsx$mgi_symbol)), ] %>% 
  select("ensembl_id", "mgi_symbol", "mgi_description", "log2FoldChange", "padj") %>% 
  rename("AB_log2FoldChange" = "log2FoldChange", "AB_padj" = "padj")

CD_intersect_tmp <-  Deg_CD_xlsx[which(Deg_CD_xlsx$mgi_symbol %in% intersect(Deg_AB_xlsx$mgi_symbol, Deg_CD_xlsx$mgi_symbol)), ] %>% 
  select("ensembl_id", "mgi_symbol", "mgi_description", "log2FoldChange", "padj") %>% 
  rename("CD_log2FoldChange" = "log2FoldChange", "CD_padj" = "padj")

CD_intersect_tmp <- CD_intersect_tmp[match(AB_intersect_tmp$mgi_symbol, CD_intersect_tmp$mgi_symbol),] 

AB_CD_intersect <- cbind(AB_intersect_tmp, select(CD_intersect_tmp, "CD_log2FoldChange", "CD_padj"))

AB_CD_ABCD_diff_intersect <- list("AB_specific" = AB_specific,
                                  "CD_specific" = CD_specific,
                                  "AB_CD_intersect" = AB_CD_intersect)
openxlsx::write.xlsx(AB_CD_ABCD_diff_intersect, 
                     "~/workspace/results_dir/nk_tum_immunoedit_complete/deg_TpNK_tp2_AB_CD_comparison/AB_CD_diff_and_interactions.xlsx")

Make a small heatmap with genes, overlaping from AB and CD.

deg_name <- "TpNK_tp2_AB_CD_comparison"
deg_dir <- file.path(output_dir, paste0("deg_", deg_name, "/"))
dir.create(deg_dir)

precomputed_objects_deg_filename <- paste0(deg_name, "_dds_objects.RData")
precomputed_objects_deg_file <- file.path(deg_dir, precomputed_objects_deg_filename)

precomputed_transf_objects_deg_filename <- paste0(deg_name, "_log2_vsd_filt_objects.RData")
precomputed_transf_objects_deg_file <- file.path(deg_dir, precomputed_transf_objects_deg_filename)

experiment_subset <- c("EXP9.4", "EXP9.5", "EXP9.6", "EXP9.7")

AB_CD_intersect_genes <- intersect(AB_results$results_signif$mgi_symbol, CD_results$results_signif$mgi_symbol)

AB_res_subset <- AB_results$results_signif[AB_results$results_signif$mgi_symbol %in% AB_CD_intersect_genes,]
#CD_res_subset <- CD_results$results_signif[CD_results$results_signif$mgi_symbol %in% AB_CD_intersect_genes,]

AB_res_subset_UP <- AB_res_subset[AB_res_subset$log2FoldChange > 0,]
AB_res_subset_DOWN <- AB_res_subset[AB_res_subset$log2FoldChange < 0,]

#AB_res_subset_UP <- AB_res_subset_UP %>% arrange(desc(log2FoldChange)) %>% slice_head(n=20)
#AB_res_subset_DOWN <- AB_res_subset_DOWN %>% arrange(log2FoldChange) %>% slice_head(n=10)

#OR padj
AB_res_subset_UP <- AB_res_subset_UP %>% arrange(padj) %>% slice_head( n = 20)
AB_res_subset_DOWN <- AB_res_subset_DOWN %>% arrange(desc(padj)) %>% slice_head(n=10)

geneset <- c(AB_res_subset_UP$mgi_symbol, AB_res_subset_DOWN$mgi_symbol)


deg_name <- "TpNK_tp2_vs_Tonly_tp1_AB"
deg_dir <- file.path(output_dir, paste0("deg_", deg_name, "/"))

precomputed_objects_deg_filename <- paste0(deg_name, "_dds_objects.RData")
precomputed_objects_deg_file <- file.path(deg_dir, precomputed_objects_deg_filename)

precomputed_transf_objects_deg_filename <- paste0(deg_name, "_log2_vsd_filt_objects.RData")
precomputed_transf_objects_deg_file <- file.path(deg_dir, precomputed_transf_objects_deg_filename)

load(precomputed_objects_deg_file)
load(precomputed_transf_objects_deg_file)

transf_object <- vsd_subset_filt
transf_batch_NObatch_experiment <- vsd_subset_filt
transf_batch_NObatch_experiment_count <- limma::removeBatchEffect(SummarizedExperiment::assay(transf_batch_NObatch_experiment), transf_batch_NObatch_experiment$experiment)
SummarizedExperiment::assay(transf_batch_NObatch_experiment) <- transf_batch_NObatch_experiment_count

metadata_heatmap <- as.data.frame(colData(dds_subset))
heatmap_counts <- SummarizedExperiment::assay(transf_object)
heatmap_counts_NObatch <- SummarizedExperiment::assay(transf_batch_NObatch_experiment) # removed xperiment batch effects! ? use experimen+cell_line removed???

heatmap_counts_deg <- heatmap_counts[rownames(heatmap_counts) %in% deg_TpNK_tp2_vs_Tonly_tp1_results$results_signif$ensembl_id, ]
heatmap_counts_NObatch_deg <- heatmap_counts_NObatch[rownames(heatmap_counts_NObatch) %in% deg_TpNK_tp2_vs_Tonly_tp1_results$results_signif$ensembl_id, ]

annotation_col <- metadata_heatmap %>%
    dplyr::select(experiment, cell_line_label, condition_tp) %>% # condition, timepoint_cell_harvesting, 
    dplyr::arrange(condition_tp, cell_line_label)

heatmap_counts_deg_ord <- heatmap_counts_deg[, match(rownames(annotation_col), colnames(heatmap_counts_deg))]
heatmap_counts_NObatch_deg_ord <- heatmap_counts_NObatch_deg[, match(rownames(annotation_col), colnames(heatmap_counts_NObatch_deg))]


ensembl2symbol_annot <- deg_TpNK_tp2_vs_Tonly_tp1_results$results_all %>%
    dplyr::select(ensembl_id, mgi_symbol)

ensembl_gene_set <- ensembl2symbol_annot[which(ensembl2symbol_annot$mgi_symbol %in% geneset),]
ensembl_gene_set <- ensembl_gene_set[ match(geneset , ensembl_gene_set$mgi_symbol),]


indexes <- which(row.names(heatmap_counts_NObatch_deg_ord) %in% ensembl_gene_set$ensembl_id)
heatmap_counts_NObatch_deg_ord <- heatmap_counts_NObatch_deg_ord[indexes,]
heatmap_counts_NObatch_deg_ord <- heatmap_counts_NObatch_deg_ord[ match(ensembl_gene_set$ensembl_id , row.names(heatmap_counts_NObatch_deg_ord)),]


rownames(heatmap_counts_NObatch_deg_ord) <- ensembl_gene_set$mgi_symbol

color.scheme <- rev(RColorBrewer::brewer.pal(8,"RdBu")) # generate the color scheme to use

ann_colors = list(
    experiment = c( EXP9.5 = "#005f73", EXP9.6 = "#0a9396", EXP9.7 = "#94d2bd"),
    condition_tp = c(Tumor_only_timepoint_1 = "#DD3344", Tumor_plus_NK_timepoint_1 = "#FF9F1C",  Tumor_plus_NK_timepoint_2 = "#553388"),
    cell_line_label = c(A = "#E9D8A6", B = "#D9BE6D")
)

heatmap_corrected <- pheatmap::pheatmap(heatmap_counts_NObatch_deg_ord,
                                        main = "Heatmap of signif. DEG - Batch Corrected",
                                        scale = "row",
                                        annotation_col = annotation_col,
                                        annotation_colors = ann_colors,
                                        #annotation_row = row_annot_symbols,
                                        show_colnames = FALSE,
                                        show_rownames = TRUE,
                                        cluster_cols = FALSE,
                                        cluster_rows = FALSE,
                                        color = color.scheme,
                                        fontsize = 10, fontsize_row = 10 #height=10, cellwidth = 11, cellheight = 11
) 

<img src=“/home/rstudio/workspace/nk_tum_immunoediting_files/figure-html/03 AB_CD”heatmap - overlap of genes from AB and CD”-1.png” width=“672” />

ggsave(filename = paste0(deg_dir, "publication/", "TpNK_tp2_vs_Tonly_tp1_heatmap_corrected_small_shared_gene_set_padj.eps"), 
       plot=heatmap_corrected,
       width = 30, height = 14, units = "cm")

ggsave(filename = paste0(deg_dir, "publication/", "TpNK_tp2_vs_Tonly_tp1_heatmap_corrected_small_shared_gene_set_padj.png"), 
       plot=heatmap_corrected,
       width = 30, height = 14, units = "cm")



##### The same but for CD #####

deg_name <- "TpNK_tp2_vs_Tonly_tp1_CD"
deg_dir <- file.path(output_dir, paste0("deg_", deg_name, "/"))

precomputed_objects_deg_filename <- paste0(deg_name, "_dds_objects.RData")
precomputed_objects_deg_file <- file.path(deg_dir, precomputed_objects_deg_filename)

precomputed_transf_objects_deg_filename <- paste0(deg_name, "_log2_vsd_filt_objects.RData")
precomputed_transf_objects_deg_file <- file.path(deg_dir, precomputed_transf_objects_deg_filename)

load(precomputed_objects_deg_file)
load(precomputed_transf_objects_deg_file)

transf_object <- vsd_subset_filt
transf_batch_NObatch_experiment <- vsd_subset_filt
transf_batch_NObatch_experiment_count <- limma::removeBatchEffect(SummarizedExperiment::assay(transf_batch_NObatch_experiment), transf_batch_NObatch_experiment$experiment)
SummarizedExperiment::assay(transf_batch_NObatch_experiment) <- transf_batch_NObatch_experiment_count

metadata_heatmap <- as.data.frame(colData(dds_subset))
heatmap_counts <- SummarizedExperiment::assay(transf_object)
heatmap_counts_NObatch <- SummarizedExperiment::assay(transf_batch_NObatch_experiment) # removed xperiment batch effects! ? use experimen+cell_line removed???

heatmap_counts_deg <- heatmap_counts[rownames(heatmap_counts) %in% deg_TpNK_tp2_vs_Tonly_tp1_results$results_signif$ensembl_id, ]
heatmap_counts_NObatch_deg <- heatmap_counts_NObatch[rownames(heatmap_counts_NObatch) %in% deg_TpNK_tp2_vs_Tonly_tp1_results$results_signif$ensembl_id, ]

annotation_col <- metadata_heatmap %>%
    dplyr::select(experiment, cell_line_label, condition_tp) %>% # condition, timepoint_cell_harvesting, 
    dplyr::arrange(condition_tp, cell_line_label)

heatmap_counts_deg_ord <- heatmap_counts_deg[, match(rownames(annotation_col), colnames(heatmap_counts_deg))]
heatmap_counts_NObatch_deg_ord <- heatmap_counts_NObatch_deg[, match(rownames(annotation_col), colnames(heatmap_counts_NObatch_deg))]


ensembl2symbol_annot <- deg_TpNK_tp2_vs_Tonly_tp1_results$results_all %>%
    dplyr::select(ensembl_id, mgi_symbol)

ensembl_gene_set <- ensembl2symbol_annot[which(ensembl2symbol_annot$mgi_symbol %in% geneset),]
ensembl_gene_set <- ensembl_gene_set[ match(geneset , ensembl_gene_set$mgi_symbol),]


indexes <- which(row.names(heatmap_counts_NObatch_deg_ord) %in% ensembl_gene_set$ensembl_id)
heatmap_counts_NObatch_deg_ord <- heatmap_counts_NObatch_deg_ord[indexes,]
heatmap_counts_NObatch_deg_ord <- heatmap_counts_NObatch_deg_ord[ match(ensembl_gene_set$ensembl_id , row.names(heatmap_counts_NObatch_deg_ord)),]


rownames(heatmap_counts_NObatch_deg_ord) <- ensembl_gene_set$mgi_symbol

color.scheme <- rev(RColorBrewer::brewer.pal(8,"RdBu")) # generate the color scheme to use

ann_colors = list(
  experiment = c(EXP9.4 = "#00262E", EXP9.5 = "#005f73", EXP9.6 = "#0a9396", EXP9.7 = "#94d2bd"),
  condition_tp = c(Tumor_only_timepoint_1 = "#DD3344", Tumor_plus_NK_timepoint_1 = "#FF9F1C", Tumor_plus_NK_timepoint_2 = "#553388"),
  cell_line_label = c(C = "#E9D8A6", D = "#D9BE6D")
)


heatmap_corrected <- pheatmap::pheatmap(heatmap_counts_NObatch_deg_ord,
                                        main = "Heatmap of signif. DEG - Batch Corrected",
                                        scale = "row",
                                        annotation_col = annotation_col,
                                        annotation_colors = ann_colors,
                                        #annotation_row = row_annot_symbols,
                                        show_colnames = FALSE,
                                        show_rownames = TRUE,
                                        cluster_cols = FALSE,
                                        cluster_rows = FALSE,
                                        color = color.scheme,
                                        
                                        fontsize = 10, fontsize_row = 10 #height=10, cellwidth = 11, cellheight = 11
) 

<img src=“/home/rstudio/workspace/nk_tum_immunoediting_files/figure-html/03 AB_CD”heatmap - overlap of genes from AB and CD”-2.png” width=“672” />

ggsave(filename = paste0(deg_dir, "publication/", "TpNK_tp2_vs_Tonly_tp1_heatmap_corrected_small_shared_gene_set_padj.eps"), 
       plot=heatmap_corrected,
       width = 30, height = 14, units = "cm")

ggsave(filename = paste0(deg_dir, "publication/", "TpNK_tp2_vs_Tonly_tp1_heatmap_corrected_small_shared_gene_set_padj.png"), 
       plot=heatmap_corrected,
       width = 30, height = 14, units = "cm")
#First, I need to load full Dataset:

experiment_name="nk_tum_immunoedit_complete"
output_dir <- "/home/rstudio/workspace/results_dir/nk_tum_immunoedit_complete/" 
precomputed_objects_filename <- paste0(experiment_name, "_dds_objects.RData")
precomputed_objects_file <- file.path(output_dir, precomputed_objects_filename)

# log2 and vsd transformed
precomputed_transf_objects_filename <- paste0(experiment_name, "_log2_vsd_filt_objects.RData")
precomputed_transf_objects_file <- file.path(output_dir, precomputed_transf_objects_filename)

# check if object loaded if not load
if(!exists(precomputed_objects_file)){
    load(precomputed_objects_file)
    load(precomputed_transf_objects_file)
} 
experiment_subset <- c("EXP9.4", "EXP9.5", "EXP9.6", "EXP9.7")
condition_tp_subset <- c("Tumor_only_timepoint_1",
                         "Tumor_plus_NK_timepoint_2",
                         "Tumor_plus_NK_timepoint_1",
                         "Tumor_plus_WT_NK_timepoint_2")
abs_filt_samples=3
deg_design <- as.formula("~ experiment + cell_line_label + condition_tp")


dds_subset <- dds_filt[ , dds_filt$condition_tp %in% condition_tp_subset]  
dds_subset <- dds_subset[ , dds_subset$experiment %in% experiment_subset]
dds_subset$condition_tp <- droplevels(dds_subset$condition_tp)
table(dds_subset$condition_tp)
## 
##    Tumor_only_timepoint_1 Tumor_plus_NK_timepoint_1 Tumor_plus_NK_timepoint_2 
##                        42                        42                        41
dds_subset$experiment <- droplevels(dds_subset$experiment)
table(dds_subset$experiment)
## 
## EXP9.4 EXP9.5 EXP9.6 EXP9.7 
##     18     36     35     36
dds_subset$cell_line_label <- droplevels(dds_subset$cell_line_label)
table(dds_subset$cell_line_label)
## 
##  A  B  C  D 
## 27 27 36 35
dds_subset_filt <- filterDatasets(dds_subset, 
                                  abs_filt = TRUE, 
                                  abs_filt_samples = abs_filt_samples) # at least in N samples, which is a smallest group size
## Original dds object samples:  125  genes:  15430 
## Minimum number of samples with expression: 3 
## Number of filtered genes: 433 
## Filtered dds object has samples: 125 genes: 14997
cat("... dds\n")
## ... dds
design(dds_subset_filt) <- deg_design
dds_subset_filt <- DESeq2::estimateSizeFactors(dds_subset_filt)
dds_subset_filt <- DESeq2::DESeq(dds_subset_filt) # do not replace outliers based on replicates
log2_norm_subset_filt <- DESeq2::normTransform(dds_subset_filt)
vsd_subset_filt <- DESeq2::vst(dds_subset_filt, blind = FALSE) 
transf_batch_NObatch_experiment <- vsd_subset_filt
transf_batch_NObatch_experiment_count <- limma::removeBatchEffect(SummarizedExperiment::assay(transf_batch_NObatch_experiment), transf_batch_NObatch_experiment$experiment)
SummarizedExperiment::assay(transf_batch_NObatch_experiment) <- transf_batch_NObatch_experiment_count
deg_TpNK_tp2_vs_Tonly_tp1_results <- generateResults_upd(dds_object = dds_subset_filt, 
                                                         coeff_name = "condition_tp_Tumor_plus_NK_timepoint_2_vs_Tumor_only_timepoint_1",
                                                         cond_numerator = "Tumor_plus_NK_timepoint_2", 
                                                         cond_denominator = "Tumor_only_timepoint_1",
                                                         cond_variable="condition_tp")
 deg_TpNK_tp2_vs_Tonly_tp1_results
## $results_signif
##            ensembl_id mgi_symbol                                         mgi_description entrezgene
## 1  ENSMUSG00000075602       Ly6a                   lymphocyte antigen 6 complex, locus A     110454
## 2  ENSMUSG00000060675     Plaat3                   phospholipase A and acyltransferase 3     225845
## 3  ENSMUSG00000033880   Lgals3bp lectin, galactoside-binding, soluble, 3 binding protein      19039
## 4  ENSMUSG00000064215      Ifi27                  interferon, alpha-inducible protein 27      52668
## 5  ENSMUSG00000104713       Gbp6                             guanylate binding protein 6     100702
## 6  ENSMUSG00000033355       Rtp4                          receptor transporter protein 4      67775
## 7  ENSMUSG00000026104      Stat1      signal transducer and activator of transcription 1      20846
## 8  ENSMUSG00000054072      Iigp1                           interferon inducible GTPase 1      60440
## 9  ENSMUSG00000061232      H2-K1                      histocompatibility 2, K1, K region      14972
## 10 ENSMUSG00000073555     Gm4951                                     predicted gene 4951     240327
##      baseMean MeanExpr_Tumor_only_timepoint_1 MeanExpr_Tumor_plus_NK_timepoint_2 log2FoldChange
## 1  2456.40621                      317.387691                         3080.57543      3.2258807
## 2   242.32525                       61.223411                          324.05583      2.3592201
## 3    96.84177                       11.627393                          119.52526      3.3142561
## 4   364.12250                      153.624891                          426.71025      1.4770706
## 5   128.50281                        6.047601                          135.68641      4.1428494
## 6    39.88500                        3.053751                           46.64006      3.7056800
## 7   342.79939                       79.385613                          393.15858      1.9873128
## 8  1112.75679                       42.307708                         1175.32669      4.3614229
## 9  4893.62031                     3035.265875                         5424.20556      0.8243463
## 10   95.71447                       10.450136                          109.08363      2.9734537
##         lfcSE FoldChange        pvalue          padj   gene_biotype chromosome_name start_position
## 1  0.10740032   9.355928 9.066006e-206 1.331071e-201 protein_coding              15       74994877
## 2  0.09136706   5.130929 2.798105e-150 2.054089e-146 protein_coding              19        7557459
## 3  0.15494094   9.946963 1.049868e-103 5.138053e-100 protein_coding              11      118392751
## 4  0.06968169   2.783829 6.176427e-102  2.267058e-98 protein_coding              12      103434211
## 5  0.23046748  17.665338  3.423172e-77  1.005180e-73 protein_coding               5      105270702
## 6  0.21981886  13.047305  6.073340e-65  1.486146e-61 protein_coding              16       23520291
## 7  0.12601002   3.964978  5.014857e-61  1.051830e-57 protein_coding               1       52119440
## 8  0.29663413  20.555077  5.585136e-58  1.025012e-54 protein_coding              18       60376027
## 9  0.05356110   1.770732  6.395728e-55  1.043356e-51 protein_coding              17       33996017
## 10 0.20013176   7.854142  1.474542e-54  2.164923e-51 protein_coding              18       60212080
##    end_position strand Tumor_only_timepoint_1-S_4_R0108 Tumor_only_timepoint_1-S_5_R0108
## 1      74998031     -1                      226.3811564                       341.817725
## 2       7588545      1                       28.0879052                        43.238856
## 3     118402092     -1                        1.7388754                        14.990335
## 4     103440239      1                       98.6588702                       122.576546
## 5     105293698     -1                        1.7271571                        14.889315
## 6      23614222      1                        0.8639205                         5.319718
## 7      52161865      1                       74.6331721                        79.944903
## 8      60392634      1                       29.1684966                        69.538617
## 9      34000347     -1                     2565.3386318                      2781.017989
## 10     60247820      1                        7.8307557                        15.001477
##    Tumor_only_timepoint_1-S_6_R0108 Tumor_only_timepoint_1-S_7_R0108
## 1                        288.630468                       148.275585
## 2                         39.019215                        34.333732
## 3                          6.625686                        13.855399
## 4                        100.911393                       160.638370
## 5                          3.290518                         5.004374
## 6                          0.000000                         2.503178
## 7                         61.756281                        70.215071
## 8                         59.599173                        54.614600
## 9                       2583.232844                      2773.171511
## 10                        16.576527                         5.042072
##    Tumor_only_timepoint_1-S_8_R0108 Tumor_only_timepoint_1-S_9_R0108
## 1                        273.597302                       161.953437
## 2                         65.456486                        53.728279
## 3                         18.428030                         6.908138
## 4                        155.662949                       139.898918
## 5                         12.920360                         0.000000
## 6                          7.539861                         6.864301
## 7                        109.368560                        91.134837
## 8                        118.646154                        30.616653
## 9                       2779.194878                      2806.570115
## 10                        10.848075                        11.522122
##    Tumor_only_timepoint_1-S_34_R0108 Tumor_only_timepoint_1-S_35_R0108
## 1                         297.822212                        351.120560
## 2                          64.761167                         60.833866
## 3                           6.168086                          4.108498
## 4                          75.784308                        108.644603
## 5                           6.126519                          5.441081
## 6                           1.225789                          4.082427
## 7                          71.821417                         79.658971
## 8                          30.861415                         41.956265
## 9                        3064.580619                       3345.985933
## 10                          9.876273                          8.223104
##    Tumor_only_timepoint_1-S_36_R0108 Tumor_only_timepoint_1-S_37_R0108
## 1                         420.097841                        290.666139
## 2                          47.844025                         43.135620
## 3                           8.124189                          2.359208
## 4                          78.863488                        126.747439
## 5                          16.138880                          0.000000
## 6                           6.727196                          0.000000
## 7                         106.799762                        101.535313
## 8                          41.444818                         27.141134
## 9                        2950.083088                       3153.145857
## 10                         18.970531                          2.360961
##    Tumor_only_timepoint_1-S_38_R0108 Tumor_only_timepoint_1-S_39_R0108
## 1                         402.564866                        406.966531
## 2                          57.841038                         84.857099
## 3                           9.171110                         19.052192
## 4                         178.052411                        166.780680
## 5                           7.085016                          6.678988
## 6                           2.025092                          4.454422
## 7                          97.821519                         96.012927
## 8                          82.837569                         97.044997
## 9                        3091.523270                       3465.008042
## 10                         12.237236                         12.337052
##    Tumor_only_timepoint_1-S_40_R0108 Tumor_only_timepoint_1-S_41_R0108
## 1                         316.059288                         457.71021
## 2                          55.206801                          64.08933
## 3                          16.929106                          17.31344
## 4                         139.644242                         135.22537
## 5                           6.005364                           9.55376
## 6                           4.806194                           6.69028
## 7                          52.263737                          88.42372
## 8                          28.980145                         101.49043
## 9                        2911.020108                        3081.24480
## 10                         14.521448                          23.10175
##    Tumor_only_timepoint_1-S_42_R0108 Tumor_only_timepoint_1-S_43_R0108
## 1                          480.48704                        199.771467
## 2                           54.28901                         81.112141
## 3                           32.73265                         20.566199
## 4                          185.68004                        215.221101
## 5                           19.73947                          9.676233
## 6                            4.64642                          3.226688
## 7                          103.96059                         87.287023
## 8                          104.66037                         34.836180
## 9                         3006.12358                       3010.527465
## 10                          14.03871                         11.915597
##    Tumor_only_timepoint_1-S_44_R0108 Tumor_only_timepoint_1-S_45_R0108
## 1                         229.257848                        203.016839
## 2                         101.769822                         60.487194
## 3                          19.150569                         18.594181
## 4                         203.126981                        201.245183
## 5                           0.000000                          0.000000
## 6                           1.189315                          8.211639
## 7                          95.951230                         66.074768
## 8                          44.097532                         36.432202
## 9                        2601.755822                       3026.664063
## 10                          5.989001                          8.270223
##    Tumor_only_timepoint_1-S_70_R0108 Tumor_only_timepoint_1-S_71_R0108
## 1                         440.139715                        323.892923
## 2                          48.336005                         57.581463
## 3                          11.796622                          9.871678
## 4                         110.169108                         89.218043
## 5                          15.232262                          8.715691
## 6                           1.172176                          1.089893
## 7                         100.600029                         66.246309
## 8                         158.691062                         73.283414
## 9                        2943.392418                       3247.204251
## 10                          7.083234                          7.683679
##    Tumor_only_timepoint_1-S_72_R0108 Tumor_only_timepoint_1-S_73_R0108
## 1                         265.136089                        459.069572
## 2                          28.606686                         43.864671
## 3                           6.839694                         19.749736
## 4                         111.856885                        106.104620
## 5                           0.000000                         22.559139
## 6                           3.883595                          4.906103
## 7                          94.042735                         90.517228
## 8                          37.192036                        104.972199
## 9                        2796.872346                       2890.697668
## 10                          4.889127                         13.835091
##    Tumor_only_timepoint_1-S_74_R0108 Tumor_only_timepoint_1-S_75_R0108
## 1                         354.799205                        318.324971
## 2                          57.523615                         59.434830
## 3                           5.907787                          4.709775
## 4                          85.429245                         83.949419
## 5                           3.520785                          7.017053
## 6                           1.174060                          2.339944
## 7                          84.495940                         81.791847
## 8                          14.314338                         39.161395
## 9                        2474.143255                       2743.189753
## 10                         15.371664                         10.604869
##    Tumor_only_timepoint_1-S_76_R0108 Tumor_only_timepoint_1-S_77_R0108
## 1                         174.547255                        289.569693
## 2                          70.389334                         88.539703
## 3                           6.577640                         13.916724
## 4                         100.399813                        165.883122
## 5                           0.000000                         15.797644
## 6                           5.228720                          5.926462
## 7                          53.714433                         82.068469
## 8                          23.102968                         86.155960
## 9                        2747.646997                       3257.905502
## 10                          3.949517                         11.937487
##    Tumor_only_timepoint_1-S_78_R0108 Tumor_only_timepoint_1-S_79_R0108
## 1                         226.481125                        217.422651
## 2                          52.089351                         71.157408
## 3                           6.879499                         12.983855
## 4                         114.182699                        178.061376
## 5                          11.388562                          8.290515
## 6                           1.139307                          9.215331
## 7                          74.665224                         70.239239
## 8                          43.979256                         44.271868
## 9                        2742.242177                       3008.792871
## 10                         11.474353                          9.281076
##    Tumor_only_timepoint_1-S_80_R0108 Tumor_only_timepoint_1-S_81_R0108
## 1                         237.785720                        199.559990
## 2                          65.070061                         73.968560
## 3                           9.310747                         13.759217
## 4                         193.700233                        226.385809
## 5                           6.473601                          9.461419
## 6                           3.700666                          7.361795
## 7                          81.003262                         83.840786
## 8                          48.784798                         33.382367
## 9                        2553.959914                       2921.514493
## 10                         11.181201                         10.591880
##    Tumor_only_timepoint_1-S_1_R0114_L5311 Tumor_only_timepoint_1-S_2_R0114_L5311
## 1                             505.9655486                            534.8699989
## 2                              60.9952416                             83.8498816
## 3                               6.0972113                              9.4535404
## 4                             138.0414336                            148.5011370
## 5                               1.1011131                              1.1177423
## 6                               0.5507746                              0.6709679
## 7                              88.9047202                             39.0957467
## 8                               1.5496474                              4.3073388
## 9                            4343.4360644                           4697.6721201
## 10                              6.1017433                              1.3515096
##    Tumor_only_timepoint_1-S_3_R0114_L5311 Tumor_only_timepoint_1-S_4_R0114_L5311
## 1                              398.278202                            349.7592985
## 2                               81.552390                             20.4637753
## 3                                0.000000                             12.7626253
## 4                              125.031832                            117.7537527
## 5                                4.732983                              2.2370502
## 6                                0.000000                              0.0000000
## 7                               74.366562                             76.4479256
## 8                                3.227663                              0.6996231
## 9                             3541.985255                           2258.0354224
## 10                               3.577009                              6.7617062
##    Tumor_only_timepoint_1-S_5_R0114_L5311 Tumor_only_timepoint_1-S_6_R0114_L5311
## 1                             400.4039886                            378.5871716
## 2                              36.2312671                             32.1178943
## 3                               3.6620692                              0.7574725
## 4                             150.1706045                            186.3597761
## 5                               0.7274781                              0.0000000
## 6                               2.1832985                              0.0000000
## 7                              83.9293788                             81.2914481
## 8                               5.2486655                              4.9412632
## 9                            3216.1610799                           2904.2991488
## 10                             16.8580396                              5.3062488
##    Tumor_only_timepoint_1-S_7_R0114_L5311 Tumor_only_timepoint_1-S_8_R0114_L5311
## 1                              405.189817                             345.041272
## 2                              136.987058                              66.942635
## 3                               27.320185                              11.367242
## 4                              270.690680                             216.884210
## 5                                2.713607                               1.254515
## 6                                0.000000                               0.000000
## 7                               56.265323                              64.204041
## 8                                0.000000                               1.177025
## 9                             4556.187967                            2642.948039
## 10                              17.315645                               6.319829
##    Tumor_only_timepoint_1-S_9_R0114_L5311 Tumor_only_timepoint_1-S_10_R0114_L5311
## 1                              321.561946                             303.5678649
## 2                               64.563574                              57.1540398
## 3                                4.263492                              10.2217886
## 4                              189.936624                             226.7748238
## 5                                4.940553                               0.0000000
## 6                                0.000000                               1.5626037
## 7                               65.269987                              83.8668240
## 8                                5.284401                               0.7327513
## 9                             3465.607292                            3154.0607381
## 10                               4.977771                               5.5081311
##    Tumor_only_timepoint_1-S_11_R0114_L5311 Tumor_only_timepoint_1-S_12_R0114_L5311
## 1                              177.4169334                              206.715578
## 2                               85.4001696                               88.472046
## 3                               26.9457231                               16.380283
## 4                              240.2594438                              283.137834
## 5                                0.0000000                                2.440484
## 6                                0.8924911                                4.882902
## 7                               62.2162061                               60.448264
## 8                                3.3481249                                5.148823
## 9                             2508.3364761                             2868.686873
## 10                              25.1680349                               13.113966
##    Tumor_plus_NK_timepoint_2-S_16_R0108 Tumor_plus_NK_timepoint_2-S_17_R0108
## 1                            1747.63424                           2043.64240
## 2                             181.27471                            176.89550
## 3                              84.16812                            115.67506
## 4                             379.78296                            376.28100
## 5                              28.98165                             32.40643
## 6                              42.37457                             63.85612
## 7                             122.06926                            141.00851
## 8                             106.58488                            116.17965
## 9                            6905.47407                           6449.94761
## 10                             24.70767                             37.59760
##    Tumor_plus_NK_timepoint_2-S_18_R0108 Tumor_plus_NK_timepoint_2-S_19_R0108
## 1                            1961.66038                           2169.31465
## 2                             208.42627                            252.79548
## 3                             131.79911                            126.70198
## 4                             393.85502                            640.51907
## 5                              38.00639                             67.76438
## 6                              63.36908                             58.10676
## 7                             166.78004                            210.28623
## 8                             203.35784                            306.30693
## 9                            6295.43975                           5827.26955
## 10                             44.67482                             32.51184
##    Tumor_plus_NK_timepoint_2-S_20_R0108 Tumor_plus_NK_timepoint_2-S_21_R0108
## 1                            1663.59370                           1719.85123
## 2                             297.94389                            307.26016
## 3                             148.16929                            124.86695
## 4                             566.98286                            551.29567
## 5                              40.31177                             56.01150
## 6                              59.30549                             44.02647
## 7                             197.19845                            203.08716
## 8                             240.53894                            271.33505
## 9                            5807.39212                           5961.36343
## 10                             27.47515                             40.30960
##    Tumor_plus_NK_timepoint_2-S_58_R0108 Tumor_plus_NK_timepoint_2-S_59_R0108
## 1                            6488.80633                           6112.69599
## 2                             503.96062                            519.00145
## 3                             197.54182                            172.40962
## 4                             600.59927                            521.49861
## 5                             549.18734                            461.83058
## 6                              82.96721                             61.25829
## 7                            1193.43729                           1284.76246
## 8                            4667.39493                           4185.81629
## 9                            6607.43373                           6610.74395
## 10                            335.25549                            308.47662
##    Tumor_plus_NK_timepoint_2-S_60_R0108 Tumor_plus_NK_timepoint_2-S_61_R0108
## 1                            6934.22563                           2570.28564
## 2                             517.09769                            326.79839
## 3                             141.77686                            120.59119
## 4                             608.79137                            319.08203
## 5                             528.39189                             77.95110
## 6                              57.34824                             23.77499
## 7                            1221.99259                            292.64331
## 8                            4105.83752                            573.06006
## 9                            6155.02388                           5435.19475
## 10                            297.57603                             69.91826
##    Tumor_plus_NK_timepoint_2-S_62_R0108 Tumor_plus_NK_timepoint_2-S_63_R0108
## 1                            3412.69962                           2379.91330
## 2                             331.81238                            309.76795
## 3                             128.80487                            114.34307
## 4                             353.07839                            340.29375
## 5                             176.34539                            130.09215
## 6                              17.29561                             17.55907
## 7                             384.82494                            274.10965
## 8                            1045.62747                            621.84628
## 9                            5600.59252                           5799.93438
## 10                            130.06187                            100.90474
##    Tumor_plus_NK_timepoint_2-S_64_R0108 Tumor_plus_NK_timepoint_2-S_65_R0108
## 1                             4339.3412                            4841.7061
## 2                              301.9529                             342.1000
## 3                              213.3512                             193.8010
## 4                              443.5946                             409.1050
## 5                              146.5486                             243.3223
## 6                              147.5576                             123.3321
## 7                              606.5484                             629.9075
## 8                             2337.2736                            2906.8185
## 9                             6228.9387                            5952.1540
## 10                             212.5692                             223.3637
##    Tumor_plus_NK_timepoint_2-S_66_R0108 Tumor_plus_NK_timepoint_2-S_67_R0108
## 1                             4053.6859                            5411.1298
## 2                              318.4661                             499.6300
## 3                              194.3493                             286.8203
## 4                              331.4864                             617.1513
## 5                              179.4221                             349.3431
## 6                              104.8067                             156.6732
## 7                              612.9029                             971.6622
## 8                             2402.8629                            4499.3759
## 9                             5620.9965                            6559.9749
## 10                             166.1504                             379.5810
##    Tumor_plus_NK_timepoint_2-S_68_R0108 Tumor_plus_NK_timepoint_2-S_69_R0108
## 1                             5068.2301                            5785.6571
## 2                              571.7537                             618.9396
## 3                              212.2716                             185.3203
## 4                              647.7775                             565.9427
## 5                              290.4707                             298.7725
## 6                              121.8675                             123.6037
## 7                              715.3963                             824.1747
## 8                             3518.5242                            3694.0642
## 9                             5686.0770                            5153.7038
## 10                             276.1582                             307.4031
##    Tumor_plus_NK_timepoint_2-S_94_R0108 Tumor_plus_NK_timepoint_2-S_95_R0108
## 1                            2853.68834                           3907.22481
## 2                             315.31412                            408.27858
## 3                              98.55720                            102.00342
## 4                             376.87513                            444.90837
## 5                             136.49872                            189.47599
## 6                              12.41389                             48.88943
## 7                             369.86090                            525.84934
## 8                             582.78057                           1490.16889
## 9                            6266.28309                           5556.06367
## 10                             75.01471                            102.07924
##    Tumor_plus_NK_timepoint_2-S_96_R0108 Tumor_plus_NK_timepoint_2-S_97_R0108
## 1                           3054.755772                           1612.10441
## 2                            343.314502                            268.09793
## 3                             48.835770                             79.95366
## 4                            407.082559                            210.56451
## 5                            127.347277                             48.09632
## 6                              7.643862                              0.00000
## 7                            368.781959                            129.49845
## 8                            556.921078                            155.88130
## 9                           5219.548551                           4777.95969
## 10                            80.833154                             24.79279
##    Tumor_plus_NK_timepoint_2-S_98_R0108 Tumor_plus_NK_timepoint_2-S_99_R0108
## 1                           3161.130849                          3365.620217
## 2                            335.293619                           353.560196
## 3                             94.332083                            91.340829
## 4                            258.349255                           233.479909
## 5                            151.191882                           143.746550
## 6                              9.586379                             4.714868
## 7                            397.607171                           488.790691
## 8                            964.039135                          1354.323735
## 9                           4752.982702                          5356.586333
## 10                           101.911465                           115.151248
##    Tumor_plus_NK_timepoint_2-S_100_R0108 Tumor_plus_NK_timepoint_2-S_101_R0108
## 1                             2248.20859                             3693.7522
## 2                              285.03959                              343.1971
## 3                              126.88052                              119.1217
## 4                              413.17662                              357.6320
## 5                               63.01274                              143.9118
## 6                               48.40394                               50.0309
## 7                              229.62176                              406.1431
## 8                              317.53985                             1435.0022
## 9                             5123.10203                             5736.0080
## 10                              41.94704                               99.5467
##    Tumor_plus_NK_timepoint_2-S_102_R0108 Tumor_plus_NK_timepoint_2-S_103_R0108
## 1                             2497.46950                            3970.89731
## 2                              257.96566                             393.79609
## 3                               99.21820                             230.91655
## 4                              419.01959                             561.65120
## 5                               58.51867                             146.73088
## 6                               31.59401                              83.58817
## 7                              224.34658                             568.56385
## 8                              377.55128                            1663.47315
## 9                             4886.22860                            5833.79498
## 10                              59.89536                             144.75629
##    Tumor_plus_NK_timepoint_2-S_104_R0108 Tumor_plus_NK_timepoint_2-S_25_R0114_L5311
## 1                             4100.50705                                 2451.84810
## 2                              442.56640                                  275.66064
## 3                              141.80837                                   24.51542
## 4                              678.64953                                  438.98600
## 5                              212.31066                                   53.73839
## 6                               67.29958                                    5.03997
## 7                              520.66380                                  152.25959
## 8                             1819.02296                                  112.74574
## 9                             6454.32469                                 6012.40889
## 10                             140.85471                                   48.22130
##    Tumor_plus_NK_timepoint_2-S_26_R0114_L5311 Tumor_plus_NK_timepoint_2-S_27_R0114_L5311
## 1                                 1413.182567                                2255.761783
## 2                                  196.748054                                 230.346953
## 3                                   13.427171                                  22.182839
## 4                                  312.802010                                 398.735564
## 5                                   42.510684                                  23.607159
## 6                                    7.504856                                   8.796351
## 7                                  169.835020                                 135.128724
## 8                                  130.822084                                  87.631803
## 9                                 3255.278358                                3864.183482
## 10                                  42.830920                                  12.685330
##    Tumor_plus_NK_timepoint_2-S_28_R0114_L5311 Tumor_plus_NK_timepoint_2-S_29_R0114_L5311
## 1                                1512.3967890                                 3662.44856
## 2                                 139.5622890                                  286.44692
## 3                                  57.1499529                                  142.65577
## 4                                 240.5679225                                  427.38939
## 5                                  31.4507777                                  128.38609
## 6                                   0.7673959                                   10.18098
## 7                                 119.9576377                                  369.55434
## 8                                  25.1898319                                  906.82660
## 9                                3509.5858480                                 4351.23008
## 10                                 22.4132504                                  124.62080
##    Tumor_plus_NK_timepoint_2-S_30_R0114_L5311 Tumor_plus_NK_timepoint_2-S_31_R0114_L5311
## 1                                  2026.09662                                1379.749209
## 2                                   151.23444                                 166.924585
## 3                                    65.04070                                  64.058822
## 4                                   311.23758                                 310.013115
## 5                                    44.45756                                  29.130493
## 6                                     0.00000                                   9.202746
## 7                                   216.07682                                 122.625541
## 8                                   138.24193                                  50.223875
## 9                                  3933.19026                                4127.948022
## 10                                   42.69281                                  32.439402
##    Tumor_plus_NK_timepoint_2-S_32_R0114_L5311 Tumor_plus_NK_timepoint_2-S_33_R0114_L5311
## 1                                  1007.45324                                 1090.70706
## 2                                   121.56246                                  137.02984
## 3                                    22.86767                                   34.30605
## 4                                   299.26832                                  256.26347
## 5                                    12.45582                                   17.03743
## 6                                    11.72777                                   10.37472
## 7                                    85.44529                                   88.40889
## 8                                    22.68844                                   18.53216
## 9                                  3051.54335                                 3363.10199
## 10                                   34.69610                                   26.12183
##    Tumor_plus_NK_timepoint_2-S_34_R0114_L5311 Tumor_plus_NK_timepoint_2-S_35_R0114_L5311
## 1                                  2545.25905                                 1889.68993
## 2                                   475.60652                                  375.51234
## 3                                   138.20748                                   91.00698
## 4                                   599.21008                                  413.73002
## 5                                    20.33720                                   16.11670
## 6                                    79.92778                                   25.93714
## 7                                   170.30992                                  117.15614
## 8                                    77.83998                                   52.85539
## 9                                  6938.32427                                 5117.73916
## 10                                   30.00380                                   25.41617
##    Tumor_plus_NK_timepoint_2-S_36_R0114_L5311
## 1                                 1899.567509
## 2                                  397.353609
## 3                                   99.386914
## 4                                  458.410596
## 5                                   27.913123
## 6                                    9.535085
## 7                                   90.224031
## 8                                   45.286888
## 9                                 6247.357109
## 10                                  28.809331
##  [ reached 'max' / getOption("max.print") -- omitted 480 rows ]
## 
## $de_details
##                                                               test
## 1 condition_tp_Tumor_plus_NK_timepoint_2_vs_Tumor_only_timepoint_1
##                                         design signif_genes signif_genes_UP signif_genes_DOWN
## 1 ~experiment + cell_line_label + condition_tp          490             330               160
##                                                       cutoffs
## 1 (!is.na(padj) & (padj < 0.05)) & abs(log2FoldChange) > 0.58
## 
## $results_all
##            ensembl_id mgi_symbol                                         mgi_description entrezgene
## 1  ENSMUSG00000075602       Ly6a                   lymphocyte antigen 6 complex, locus A     110454
## 2  ENSMUSG00000060675     Plaat3                   phospholipase A and acyltransferase 3     225845
## 3  ENSMUSG00000033880   Lgals3bp lectin, galactoside-binding, soluble, 3 binding protein      19039
## 4  ENSMUSG00000064215      Ifi27                  interferon, alpha-inducible protein 27      52668
## 5  ENSMUSG00000104713       Gbp6                             guanylate binding protein 6     100702
## 6  ENSMUSG00000033355       Rtp4                          receptor transporter protein 4      67775
## 7  ENSMUSG00000026104      Stat1      signal transducer and activator of transcription 1      20846
## 8  ENSMUSG00000054072      Iigp1                           interferon inducible GTPase 1      60440
## 9  ENSMUSG00000061232      H2-K1                      histocompatibility 2, K1, K region      14972
## 10 ENSMUSG00000073555     Gm4951                                     predicted gene 4951     240327
##      baseMean MeanExpr_Tumor_only_timepoint_1 MeanExpr_Tumor_plus_NK_timepoint_2 log2FoldChange
## 1  2456.40621                      317.387691                         3080.57543      3.2258807
## 2   242.32525                       61.223411                          324.05583      2.3592201
## 3    96.84177                       11.627393                          119.52526      3.3142561
## 4   364.12250                      153.624891                          426.71025      1.4770706
## 5   128.50281                        6.047601                          135.68641      4.1428494
## 6    39.88500                        3.053751                           46.64006      3.7056800
## 7   342.79939                       79.385613                          393.15858      1.9873128
## 8  1112.75679                       42.307708                         1175.32669      4.3614229
## 9  4893.62031                     3035.265875                         5424.20556      0.8243463
## 10   95.71447                       10.450136                          109.08363      2.9734537
##         lfcSE FoldChange        pvalue          padj   gene_biotype chromosome_name start_position
## 1  0.10740032   9.355928 9.066006e-206 1.331071e-201 protein_coding              15       74994877
## 2  0.09136706   5.130929 2.798105e-150 2.054089e-146 protein_coding              19        7557459
## 3  0.15494094   9.946963 1.049868e-103 5.138053e-100 protein_coding              11      118392751
## 4  0.06968169   2.783829 6.176427e-102  2.267058e-98 protein_coding              12      103434211
## 5  0.23046748  17.665338  3.423172e-77  1.005180e-73 protein_coding               5      105270702
## 6  0.21981886  13.047305  6.073340e-65  1.486146e-61 protein_coding              16       23520291
## 7  0.12601002   3.964978  5.014857e-61  1.051830e-57 protein_coding               1       52119440
## 8  0.29663413  20.555077  5.585136e-58  1.025012e-54 protein_coding              18       60376027
## 9  0.05356110   1.770732  6.395728e-55  1.043356e-51 protein_coding              17       33996017
## 10 0.20013176   7.854142  1.474542e-54  2.164923e-51 protein_coding              18       60212080
##    end_position strand Tumor_only_timepoint_1-S_4_R0108 Tumor_only_timepoint_1-S_5_R0108
## 1      74998031     -1                      226.3811564                       341.817725
## 2       7588545      1                       28.0879052                        43.238856
## 3     118402092     -1                        1.7388754                        14.990335
## 4     103440239      1                       98.6588702                       122.576546
## 5     105293698     -1                        1.7271571                        14.889315
## 6      23614222      1                        0.8639205                         5.319718
## 7      52161865      1                       74.6331721                        79.944903
## 8      60392634      1                       29.1684966                        69.538617
## 9      34000347     -1                     2565.3386318                      2781.017989
## 10     60247820      1                        7.8307557                        15.001477
##    Tumor_only_timepoint_1-S_6_R0108 Tumor_only_timepoint_1-S_7_R0108
## 1                        288.630468                       148.275585
## 2                         39.019215                        34.333732
## 3                          6.625686                        13.855399
## 4                        100.911393                       160.638370
## 5                          3.290518                         5.004374
## 6                          0.000000                         2.503178
## 7                         61.756281                        70.215071
## 8                         59.599173                        54.614600
## 9                       2583.232844                      2773.171511
## 10                        16.576527                         5.042072
##    Tumor_only_timepoint_1-S_8_R0108 Tumor_only_timepoint_1-S_9_R0108
## 1                        273.597302                       161.953437
## 2                         65.456486                        53.728279
## 3                         18.428030                         6.908138
## 4                        155.662949                       139.898918
## 5                         12.920360                         0.000000
## 6                          7.539861                         6.864301
## 7                        109.368560                        91.134837
## 8                        118.646154                        30.616653
## 9                       2779.194878                      2806.570115
## 10                        10.848075                        11.522122
##    Tumor_only_timepoint_1-S_34_R0108 Tumor_only_timepoint_1-S_35_R0108
## 1                         297.822212                        351.120560
## 2                          64.761167                         60.833866
## 3                           6.168086                          4.108498
## 4                          75.784308                        108.644603
## 5                           6.126519                          5.441081
## 6                           1.225789                          4.082427
## 7                          71.821417                         79.658971
## 8                          30.861415                         41.956265
## 9                        3064.580619                       3345.985933
## 10                          9.876273                          8.223104
##    Tumor_only_timepoint_1-S_36_R0108 Tumor_only_timepoint_1-S_37_R0108
## 1                         420.097841                        290.666139
## 2                          47.844025                         43.135620
## 3                           8.124189                          2.359208
## 4                          78.863488                        126.747439
## 5                          16.138880                          0.000000
## 6                           6.727196                          0.000000
## 7                         106.799762                        101.535313
## 8                          41.444818                         27.141134
## 9                        2950.083088                       3153.145857
## 10                         18.970531                          2.360961
##    Tumor_only_timepoint_1-S_38_R0108 Tumor_only_timepoint_1-S_39_R0108
## 1                         402.564866                        406.966531
## 2                          57.841038                         84.857099
## 3                           9.171110                         19.052192
## 4                         178.052411                        166.780680
## 5                           7.085016                          6.678988
## 6                           2.025092                          4.454422
## 7                          97.821519                         96.012927
## 8                          82.837569                         97.044997
## 9                        3091.523270                       3465.008042
## 10                         12.237236                         12.337052
##    Tumor_only_timepoint_1-S_40_R0108 Tumor_only_timepoint_1-S_41_R0108
## 1                         316.059288                         457.71021
## 2                          55.206801                          64.08933
## 3                          16.929106                          17.31344
## 4                         139.644242                         135.22537
## 5                           6.005364                           9.55376
## 6                           4.806194                           6.69028
## 7                          52.263737                          88.42372
## 8                          28.980145                         101.49043
## 9                        2911.020108                        3081.24480
## 10                         14.521448                          23.10175
##    Tumor_only_timepoint_1-S_42_R0108 Tumor_only_timepoint_1-S_43_R0108
## 1                          480.48704                        199.771467
## 2                           54.28901                         81.112141
## 3                           32.73265                         20.566199
## 4                          185.68004                        215.221101
## 5                           19.73947                          9.676233
## 6                            4.64642                          3.226688
## 7                          103.96059                         87.287023
## 8                          104.66037                         34.836180
## 9                         3006.12358                       3010.527465
## 10                          14.03871                         11.915597
##    Tumor_only_timepoint_1-S_44_R0108 Tumor_only_timepoint_1-S_45_R0108
## 1                         229.257848                        203.016839
## 2                         101.769822                         60.487194
## 3                          19.150569                         18.594181
## 4                         203.126981                        201.245183
## 5                           0.000000                          0.000000
## 6                           1.189315                          8.211639
## 7                          95.951230                         66.074768
## 8                          44.097532                         36.432202
## 9                        2601.755822                       3026.664063
## 10                          5.989001                          8.270223
##    Tumor_only_timepoint_1-S_70_R0108 Tumor_only_timepoint_1-S_71_R0108
## 1                         440.139715                        323.892923
## 2                          48.336005                         57.581463
## 3                          11.796622                          9.871678
## 4                         110.169108                         89.218043
## 5                          15.232262                          8.715691
## 6                           1.172176                          1.089893
## 7                         100.600029                         66.246309
## 8                         158.691062                         73.283414
## 9                        2943.392418                       3247.204251
## 10                          7.083234                          7.683679
##    Tumor_only_timepoint_1-S_72_R0108 Tumor_only_timepoint_1-S_73_R0108
## 1                         265.136089                        459.069572
## 2                          28.606686                         43.864671
## 3                           6.839694                         19.749736
## 4                         111.856885                        106.104620
## 5                           0.000000                         22.559139
## 6                           3.883595                          4.906103
## 7                          94.042735                         90.517228
## 8                          37.192036                        104.972199
## 9                        2796.872346                       2890.697668
## 10                          4.889127                         13.835091
##    Tumor_only_timepoint_1-S_74_R0108 Tumor_only_timepoint_1-S_75_R0108
## 1                         354.799205                        318.324971
## 2                          57.523615                         59.434830
## 3                           5.907787                          4.709775
## 4                          85.429245                         83.949419
## 5                           3.520785                          7.017053
## 6                           1.174060                          2.339944
## 7                          84.495940                         81.791847
## 8                          14.314338                         39.161395
## 9                        2474.143255                       2743.189753
## 10                         15.371664                         10.604869
##    Tumor_only_timepoint_1-S_76_R0108 Tumor_only_timepoint_1-S_77_R0108
## 1                         174.547255                        289.569693
## 2                          70.389334                         88.539703
## 3                           6.577640                         13.916724
## 4                         100.399813                        165.883122
## 5                           0.000000                         15.797644
## 6                           5.228720                          5.926462
## 7                          53.714433                         82.068469
## 8                          23.102968                         86.155960
## 9                        2747.646997                       3257.905502
## 10                          3.949517                         11.937487
##    Tumor_only_timepoint_1-S_78_R0108 Tumor_only_timepoint_1-S_79_R0108
## 1                         226.481125                        217.422651
## 2                          52.089351                         71.157408
## 3                           6.879499                         12.983855
## 4                         114.182699                        178.061376
## 5                          11.388562                          8.290515
## 6                           1.139307                          9.215331
## 7                          74.665224                         70.239239
## 8                          43.979256                         44.271868
## 9                        2742.242177                       3008.792871
## 10                         11.474353                          9.281076
##    Tumor_only_timepoint_1-S_80_R0108 Tumor_only_timepoint_1-S_81_R0108
## 1                         237.785720                        199.559990
## 2                          65.070061                         73.968560
## 3                           9.310747                         13.759217
## 4                         193.700233                        226.385809
## 5                           6.473601                          9.461419
## 6                           3.700666                          7.361795
## 7                          81.003262                         83.840786
## 8                          48.784798                         33.382367
## 9                        2553.959914                       2921.514493
## 10                         11.181201                         10.591880
##    Tumor_only_timepoint_1-S_1_R0114_L5311 Tumor_only_timepoint_1-S_2_R0114_L5311
## 1                             505.9655486                            534.8699989
## 2                              60.9952416                             83.8498816
## 3                               6.0972113                              9.4535404
## 4                             138.0414336                            148.5011370
## 5                               1.1011131                              1.1177423
## 6                               0.5507746                              0.6709679
## 7                              88.9047202                             39.0957467
## 8                               1.5496474                              4.3073388
## 9                            4343.4360644                           4697.6721201
## 10                              6.1017433                              1.3515096
##    Tumor_only_timepoint_1-S_3_R0114_L5311 Tumor_only_timepoint_1-S_4_R0114_L5311
## 1                              398.278202                            349.7592985
## 2                               81.552390                             20.4637753
## 3                                0.000000                             12.7626253
## 4                              125.031832                            117.7537527
## 5                                4.732983                              2.2370502
## 6                                0.000000                              0.0000000
## 7                               74.366562                             76.4479256
## 8                                3.227663                              0.6996231
## 9                             3541.985255                           2258.0354224
## 10                               3.577009                              6.7617062
##    Tumor_only_timepoint_1-S_5_R0114_L5311 Tumor_only_timepoint_1-S_6_R0114_L5311
## 1                             400.4039886                            378.5871716
## 2                              36.2312671                             32.1178943
## 3                               3.6620692                              0.7574725
## 4                             150.1706045                            186.3597761
## 5                               0.7274781                              0.0000000
## 6                               2.1832985                              0.0000000
## 7                              83.9293788                             81.2914481
## 8                               5.2486655                              4.9412632
## 9                            3216.1610799                           2904.2991488
## 10                             16.8580396                              5.3062488
##    Tumor_only_timepoint_1-S_7_R0114_L5311 Tumor_only_timepoint_1-S_8_R0114_L5311
## 1                              405.189817                             345.041272
## 2                              136.987058                              66.942635
## 3                               27.320185                              11.367242
## 4                              270.690680                             216.884210
## 5                                2.713607                               1.254515
## 6                                0.000000                               0.000000
## 7                               56.265323                              64.204041
## 8                                0.000000                               1.177025
## 9                             4556.187967                            2642.948039
## 10                              17.315645                               6.319829
##    Tumor_only_timepoint_1-S_9_R0114_L5311 Tumor_only_timepoint_1-S_10_R0114_L5311
## 1                              321.561946                             303.5678649
## 2                               64.563574                              57.1540398
## 3                                4.263492                              10.2217886
## 4                              189.936624                             226.7748238
## 5                                4.940553                               0.0000000
## 6                                0.000000                               1.5626037
## 7                               65.269987                              83.8668240
## 8                                5.284401                               0.7327513
## 9                             3465.607292                            3154.0607381
## 10                               4.977771                               5.5081311
##    Tumor_only_timepoint_1-S_11_R0114_L5311 Tumor_only_timepoint_1-S_12_R0114_L5311
## 1                              177.4169334                              206.715578
## 2                               85.4001696                               88.472046
## 3                               26.9457231                               16.380283
## 4                              240.2594438                              283.137834
## 5                                0.0000000                                2.440484
## 6                                0.8924911                                4.882902
## 7                               62.2162061                               60.448264
## 8                                3.3481249                                5.148823
## 9                             2508.3364761                             2868.686873
## 10                              25.1680349                               13.113966
##    Tumor_plus_NK_timepoint_2-S_16_R0108 Tumor_plus_NK_timepoint_2-S_17_R0108
## 1                            1747.63424                           2043.64240
## 2                             181.27471                            176.89550
## 3                              84.16812                            115.67506
## 4                             379.78296                            376.28100
## 5                              28.98165                             32.40643
## 6                              42.37457                             63.85612
## 7                             122.06926                            141.00851
## 8                             106.58488                            116.17965
## 9                            6905.47407                           6449.94761
## 10                             24.70767                             37.59760
##    Tumor_plus_NK_timepoint_2-S_18_R0108 Tumor_plus_NK_timepoint_2-S_19_R0108
## 1                            1961.66038                           2169.31465
## 2                             208.42627                            252.79548
## 3                             131.79911                            126.70198
## 4                             393.85502                            640.51907
## 5                              38.00639                             67.76438
## 6                              63.36908                             58.10676
## 7                             166.78004                            210.28623
## 8                             203.35784                            306.30693
## 9                            6295.43975                           5827.26955
## 10                             44.67482                             32.51184
##    Tumor_plus_NK_timepoint_2-S_20_R0108 Tumor_plus_NK_timepoint_2-S_21_R0108
## 1                            1663.59370                           1719.85123
## 2                             297.94389                            307.26016
## 3                             148.16929                            124.86695
## 4                             566.98286                            551.29567
## 5                              40.31177                             56.01150
## 6                              59.30549                             44.02647
## 7                             197.19845                            203.08716
## 8                             240.53894                            271.33505
## 9                            5807.39212                           5961.36343
## 10                             27.47515                             40.30960
##    Tumor_plus_NK_timepoint_2-S_58_R0108 Tumor_plus_NK_timepoint_2-S_59_R0108
## 1                            6488.80633                           6112.69599
## 2                             503.96062                            519.00145
## 3                             197.54182                            172.40962
## 4                             600.59927                            521.49861
## 5                             549.18734                            461.83058
## 6                              82.96721                             61.25829
## 7                            1193.43729                           1284.76246
## 8                            4667.39493                           4185.81629
## 9                            6607.43373                           6610.74395
## 10                            335.25549                            308.47662
##    Tumor_plus_NK_timepoint_2-S_60_R0108 Tumor_plus_NK_timepoint_2-S_61_R0108
## 1                            6934.22563                           2570.28564
## 2                             517.09769                            326.79839
## 3                             141.77686                            120.59119
## 4                             608.79137                            319.08203
## 5                             528.39189                             77.95110
## 6                              57.34824                             23.77499
## 7                            1221.99259                            292.64331
## 8                            4105.83752                            573.06006
## 9                            6155.02388                           5435.19475
## 10                            297.57603                             69.91826
##    Tumor_plus_NK_timepoint_2-S_62_R0108 Tumor_plus_NK_timepoint_2-S_63_R0108
## 1                            3412.69962                           2379.91330
## 2                             331.81238                            309.76795
## 3                             128.80487                            114.34307
## 4                             353.07839                            340.29375
## 5                             176.34539                            130.09215
## 6                              17.29561                             17.55907
## 7                             384.82494                            274.10965
## 8                            1045.62747                            621.84628
## 9                            5600.59252                           5799.93438
## 10                            130.06187                            100.90474
##    Tumor_plus_NK_timepoint_2-S_64_R0108 Tumor_plus_NK_timepoint_2-S_65_R0108
## 1                             4339.3412                            4841.7061
## 2                              301.9529                             342.1000
## 3                              213.3512                             193.8010
## 4                              443.5946                             409.1050
## 5                              146.5486                             243.3223
## 6                              147.5576                             123.3321
## 7                              606.5484                             629.9075
## 8                             2337.2736                            2906.8185
## 9                             6228.9387                            5952.1540
## 10                             212.5692                             223.3637
##    Tumor_plus_NK_timepoint_2-S_66_R0108 Tumor_plus_NK_timepoint_2-S_67_R0108
## 1                             4053.6859                            5411.1298
## 2                              318.4661                             499.6300
## 3                              194.3493                             286.8203
## 4                              331.4864                             617.1513
## 5                              179.4221                             349.3431
## 6                              104.8067                             156.6732
## 7                              612.9029                             971.6622
## 8                             2402.8629                            4499.3759
## 9                             5620.9965                            6559.9749
## 10                             166.1504                             379.5810
##    Tumor_plus_NK_timepoint_2-S_68_R0108 Tumor_plus_NK_timepoint_2-S_69_R0108
## 1                             5068.2301                            5785.6571
## 2                              571.7537                             618.9396
## 3                              212.2716                             185.3203
## 4                              647.7775                             565.9427
## 5                              290.4707                             298.7725
## 6                              121.8675                             123.6037
## 7                              715.3963                             824.1747
## 8                             3518.5242                            3694.0642
## 9                             5686.0770                            5153.7038
## 10                             276.1582                             307.4031
##    Tumor_plus_NK_timepoint_2-S_94_R0108 Tumor_plus_NK_timepoint_2-S_95_R0108
## 1                            2853.68834                           3907.22481
## 2                             315.31412                            408.27858
## 3                              98.55720                            102.00342
## 4                             376.87513                            444.90837
## 5                             136.49872                            189.47599
## 6                              12.41389                             48.88943
## 7                             369.86090                            525.84934
## 8                             582.78057                           1490.16889
## 9                            6266.28309                           5556.06367
## 10                             75.01471                            102.07924
##    Tumor_plus_NK_timepoint_2-S_96_R0108 Tumor_plus_NK_timepoint_2-S_97_R0108
## 1                           3054.755772                           1612.10441
## 2                            343.314502                            268.09793
## 3                             48.835770                             79.95366
## 4                            407.082559                            210.56451
## 5                            127.347277                             48.09632
## 6                              7.643862                              0.00000
## 7                            368.781959                            129.49845
## 8                            556.921078                            155.88130
## 9                           5219.548551                           4777.95969
## 10                            80.833154                             24.79279
##    Tumor_plus_NK_timepoint_2-S_98_R0108 Tumor_plus_NK_timepoint_2-S_99_R0108
## 1                           3161.130849                          3365.620217
## 2                            335.293619                           353.560196
## 3                             94.332083                            91.340829
## 4                            258.349255                           233.479909
## 5                            151.191882                           143.746550
## 6                              9.586379                             4.714868
## 7                            397.607171                           488.790691
## 8                            964.039135                          1354.323735
## 9                           4752.982702                          5356.586333
## 10                           101.911465                           115.151248
##    Tumor_plus_NK_timepoint_2-S_100_R0108 Tumor_plus_NK_timepoint_2-S_101_R0108
## 1                             2248.20859                             3693.7522
## 2                              285.03959                              343.1971
## 3                              126.88052                              119.1217
## 4                              413.17662                              357.6320
## 5                               63.01274                              143.9118
## 6                               48.40394                               50.0309
## 7                              229.62176                              406.1431
## 8                              317.53985                             1435.0022
## 9                             5123.10203                             5736.0080
## 10                              41.94704                               99.5467
##    Tumor_plus_NK_timepoint_2-S_102_R0108 Tumor_plus_NK_timepoint_2-S_103_R0108
## 1                             2497.46950                            3970.89731
## 2                              257.96566                             393.79609
## 3                               99.21820                             230.91655
## 4                              419.01959                             561.65120
## 5                               58.51867                             146.73088
## 6                               31.59401                              83.58817
## 7                              224.34658                             568.56385
## 8                              377.55128                            1663.47315
## 9                             4886.22860                            5833.79498
## 10                              59.89536                             144.75629
##    Tumor_plus_NK_timepoint_2-S_104_R0108 Tumor_plus_NK_timepoint_2-S_25_R0114_L5311
## 1                             4100.50705                                 2451.84810
## 2                              442.56640                                  275.66064
## 3                              141.80837                                   24.51542
## 4                              678.64953                                  438.98600
## 5                              212.31066                                   53.73839
## 6                               67.29958                                    5.03997
## 7                              520.66380                                  152.25959
## 8                             1819.02296                                  112.74574
## 9                             6454.32469                                 6012.40889
## 10                             140.85471                                   48.22130
##    Tumor_plus_NK_timepoint_2-S_26_R0114_L5311 Tumor_plus_NK_timepoint_2-S_27_R0114_L5311
## 1                                 1413.182567                                2255.761783
## 2                                  196.748054                                 230.346953
## 3                                   13.427171                                  22.182839
## 4                                  312.802010                                 398.735564
## 5                                   42.510684                                  23.607159
## 6                                    7.504856                                   8.796351
## 7                                  169.835020                                 135.128724
## 8                                  130.822084                                  87.631803
## 9                                 3255.278358                                3864.183482
## 10                                  42.830920                                  12.685330
##    Tumor_plus_NK_timepoint_2-S_28_R0114_L5311 Tumor_plus_NK_timepoint_2-S_29_R0114_L5311
## 1                                1512.3967890                                 3662.44856
## 2                                 139.5622890                                  286.44692
## 3                                  57.1499529                                  142.65577
## 4                                 240.5679225                                  427.38939
## 5                                  31.4507777                                  128.38609
## 6                                   0.7673959                                   10.18098
## 7                                 119.9576377                                  369.55434
## 8                                  25.1898319                                  906.82660
## 9                                3509.5858480                                 4351.23008
## 10                                 22.4132504                                  124.62080
##    Tumor_plus_NK_timepoint_2-S_30_R0114_L5311 Tumor_plus_NK_timepoint_2-S_31_R0114_L5311
## 1                                  2026.09662                                1379.749209
## 2                                   151.23444                                 166.924585
## 3                                    65.04070                                  64.058822
## 4                                   311.23758                                 310.013115
## 5                                    44.45756                                  29.130493
## 6                                     0.00000                                   9.202746
## 7                                   216.07682                                 122.625541
## 8                                   138.24193                                  50.223875
## 9                                  3933.19026                                4127.948022
## 10                                   42.69281                                  32.439402
##    Tumor_plus_NK_timepoint_2-S_32_R0114_L5311 Tumor_plus_NK_timepoint_2-S_33_R0114_L5311
## 1                                  1007.45324                                 1090.70706
## 2                                   121.56246                                  137.02984
## 3                                    22.86767                                   34.30605
## 4                                   299.26832                                  256.26347
## 5                                    12.45582                                   17.03743
## 6                                    11.72777                                   10.37472
## 7                                    85.44529                                   88.40889
## 8                                    22.68844                                   18.53216
## 9                                  3051.54335                                 3363.10199
## 10                                   34.69610                                   26.12183
##    Tumor_plus_NK_timepoint_2-S_34_R0114_L5311 Tumor_plus_NK_timepoint_2-S_35_R0114_L5311
## 1                                  2545.25905                                 1889.68993
## 2                                   475.60652                                  375.51234
## 3                                   138.20748                                   91.00698
## 4                                   599.21008                                  413.73002
## 5                                    20.33720                                   16.11670
## 6                                    79.92778                                   25.93714
## 7                                   170.30992                                  117.15614
## 8                                    77.83998                                   52.85539
## 9                                  6938.32427                                 5117.73916
## 10                                   30.00380                                   25.41617
##    Tumor_plus_NK_timepoint_2-S_36_R0114_L5311
## 1                                 1899.567509
## 2                                  397.353609
## 3                                   99.386914
## 4                                  458.410596
## 5                                   27.913123
## 6                                    9.535085
## 7                                   90.224031
## 8                                   45.286888
## 9                                 6247.357109
## 10                                  28.809331
##  [ reached 'max' / getOption("max.print") -- omitted 14987 rows ]
metadata_heatmap <- as.data.frame(colData(dds_subset))
transf_object <- vsd_subset_filt
heatmap_counts <- SummarizedExperiment::assay(transf_object)
heatmap_counts_NObatch <- SummarizedExperiment::assay(transf_batch_NObatch_experiment) # removed xperiment batch effects! ? use experimen+cell_line removed???

heatmap_counts_NObatch_deg <- heatmap_counts_NObatch[rownames(heatmap_counts_NObatch) %in% deg_TpNK_tp2_vs_Tonly_tp1_results$results_signif$ensembl_id, ]

annotation_col <- metadata_heatmap %>%
  dplyr::select(experiment, cell_line_label, condition_tp) %>% # condition, timepoint_cell_harvesting, 
  dplyr::arrange(condition_tp, cell_line_label)

heatmap_counts_NObatch_deg_ord <- heatmap_counts_NObatch_deg[, match(rownames(annotation_col), colnames(heatmap_counts_NObatch_deg))]

ensembl2symbol_annot <- deg_TpNK_tp2_vs_Tonly_tp1_results$results_all %>%
  dplyr::select(ensembl_id, mgi_symbol)


# select top 20 and 10 gens from ab cd up and down regulated
AB_res_path <- "~/workspace/results_dir/nk_tum_immunoedit_complete/deg_TpNK_tp2_vs_Tonly_tp1_AB/deg_TpNK_tp2_vs_Tonly_tp1_AB_results.xlsx"
CD_res_path <- "~/workspace/results_dir/nk_tum_immunoedit_complete/deg_TpNK_tp2_vs_Tonly_tp1_CD/deg_TpNK_tp2_vs_Tonly_tp1_CD_results.xlsx"

AB_results <- openxlsx::read.xlsx(xlsxFile = AB_res_path, sheet = "results_signif")
CD_results <- openxlsx::read.xlsx(xlsxFile = CD_res_path, sheet = "results_signif")

AB_CD_intersect_genes <- intersect(AB_results$mgi_symbol, CD_results$mgi_symbol)

AB_res_subset <- AB_results[AB_results$mgi_symbol %in% AB_CD_intersect_genes,]
CD_res_subset <- CD_results[CD_results$mgi_symbol %in% AB_CD_intersect_genes,]

combined_AB_CD <- rbind.data.frame(AB_res_subset[,1:14], CD_res_subset[,1:14])

AB_CD_res_subset_UP <- combined_AB_CD[combined_AB_CD$log2FoldChange > 0,]
AB_CD_res_subset_DOWN <- combined_AB_CD[combined_AB_CD$log2FoldChange < 0,]

AB_CD_res_subset_UP <- AB_CD_res_subset_UP %>% arrange(padj)
AB_CD_res_subset_UP <- AB_CD_res_subset_UP[!duplicated(AB_CD_res_subset_UP$mgi_symbol),] %>% slice_head(n=20)

AB_CD_res_subset_DOWN <- AB_CD_res_subset_DOWN %>% arrange(padj)
AB_CD_res_subset_DOWN <- AB_CD_res_subset_DOWN[!duplicated(AB_CD_res_subset_DOWN$mgi_symbol),]%>% slice_head(n=10)

geneset <- c(AB_CD_res_subset_UP$mgi_symbol, AB_CD_res_subset_DOWN$mgi_symbol)


ensembl_gene_set <- ensembl2symbol_annot[which(ensembl2symbol_annot$mgi_symbol %in% geneset),]
ensembl_gene_set <- ensembl_gene_set[ match(geneset , ensembl_gene_set$mgi_symbol),]

indexes <- which(row.names(heatmap_counts_NObatch_deg_ord) %in% ensembl_gene_set$ensembl_id)
heatmap_counts_NObatch_deg_ord <- heatmap_counts_NObatch_deg_ord[indexes,]
heatmap_counts_NObatch_deg_ord <- heatmap_counts_NObatch_deg_ord[ match(ensembl_gene_set$ensembl_id , row.names(heatmap_counts_NObatch_deg_ord)),]


rownames(heatmap_counts_NObatch_deg_ord) <- ensembl_gene_set$mgi_symbol


######

color.scheme <- rev(RColorBrewer::brewer.pal(8,"RdBu")) # generate the color scheme to use

ann_colors = list(
  experiment = c(EXP9.4 = "#00262E", EXP9.5 = "#005f73", EXP9.6 = "#0a9396", EXP9.7 = "#94d2bd"),
  condition_tp = c(Tumor_only_timepoint_1 = "#DD3344", Tumor_plus_NK_timepoint_1 = "#FF9F1C", Tumor_plus_NK_timepoint_2 = "#553388"),
  cell_line_label = c( A = "#E9D8A6", B = "#D9BE6D", C = "#676F54", D = "#395B50")
)

heatmap_corrected <- pheatmap::pheatmap(heatmap_counts_NObatch_deg_ord,
                   main = "Heatmap of signif. DEG",
                   scale = "row",
                   annotation_col = annotation_col,
                   annotation_colors = ann_colors,
                   #annotation_row = row_annot_symbols,
                   show_colnames = FALSE,
                   cluster_cols = FALSE,
                   cluster_rows = FALSE,
                   show_rownames = TRUE,
                   color = color.scheme,
                   fontsize = 10, fontsize_row = 10 #height=10, cellwidth = 11, cellheight = 11
                   ) # 

<img src=“/home/rstudio/workspace/nk_tum_immunoediting_files/figure-html/03”try to plot in the big, integrated DATASET”-1.png” width=“672” />

ggsave(filename = paste0("~/workspace/results_dir/nk_tum_immunoedit_complete/deg_TpNK_tp2_AB_CD_comparison/", "TpNK_tp2_vs_Tonly_tp1_heatmap_corrected_small_shared_gene_set_padj.eps"), 
       plot=heatmap_corrected,
       width = 30, height = 14, units = "cm")

ggsave(filename = paste0("~/workspace/results_dir/nk_tum_immunoedit_complete/deg_TpNK_tp2_AB_CD_comparison/", "TpNK_tp2_vs_Tonly_tp1_heatmap_corrected_small_shared_gene_set_padj.png"), 
       plot=heatmap_corrected,
       width = 30, height = 14, units = "cm")

Results - EXP9.8 comparing WT vs KO, IFNG and PrfKO_NK timepoints

# Loading the required dependencies:
import::from(.from = variancePartition, fitExtractVarPartModel, sortCols, plotVarPart)
import::from(.from = doParallel, registerDoParallel)
import::from(.from = parallel, makeCluster, stopCluster)
# import utils scripts
import::from(.from = here::here("utils/filterDatasets.R"), "filterDatasets", .character_only=TRUE) # used for filtering
import::from(.from = here::here("utils/rnaSelectTopVarGenes.R"), "rnaSelectTopVarGenes", .character_only=TRUE)
import::from(.from = here::here("utils/edaFunctions.R"), "varPartitionEstimate", "generatePCA", "pcaExtractVariance", "pcaPlotVariance", "pcaCorrPCs", "pcaCorrPCsPlot", .character_only=TRUE)
import::from(.from = here::here("utils/generateResults.R"), "generateResults_upd", .character_only=TRUE) # 
import::from(.from = here::here("utils/plotDegResults.R"), "plotVolcano","plotVolcano_repel", .character_only=TRUE)
import::from(.from = here::here("utils/generateEnsemblAnnotation.R"), "generateEnsemblAnnotation", .character_only=TRUE) # used for filtering
#project setup
# loading config file ----
config_file <- file.path( "nk_tum_immunoediting_config.yaml")  # params$config_file; params not found? ; change to project_config.yaml
config <- yaml::read_yaml(config_file)
#config <- yaml::read_yaml(params$config_file)

experiment_name="nk_tum_immunoedit_complete_ab2" # this can be a subset analysis - e.g. just batch1,...
experiment_design="1"  # used only to construct initial dds object
abs_filt_samples=3

# parameters for annotation
biomart_host = "http://nov2020.archive.ensembl.org"
biomart_Ens_version = "Ensembl Genes 102"
biomart_dataset="mmusculus_gene_ensembl"

# directories
output_dir = paste0("./results_dir", "/",experiment_name,"/")
dir.create(output_dir)
# importing only key functions that are actually used - not to polute namespace!
import::from(.from = readr, read_csv, cols)
import::from(magrittr, "%>%")
import::from(dplyr, mutate, select, filter, rename, arrange, desc, group_by, summarise, ungroup)  # dplyr_mutate = mutate
import::from(purrr, map)
import::from(future, plan, multisession, sequential)
import::from(furrr, furrr_options, future_map2)
import::from(ggplot2, .all=TRUE) # importing all as there is too many
import::from(grid, gpar) # needed in complexheatmap
import::from(kableExtra, kable_styling, kbl)
import::from(.from = GenomicFeatures, makeTxDbFromGFF)
import::from(.from = AnnotationDbi, annot_db_keys = keys, annot_db_select = select)
import::from(.from = tximport, tximport)
import::from(.from = DESeq2, .all=TRUE)

#table(metadata$condition_tp, metadata$experiment)
# Create a list of comparisons:
# Full list
# list_of_comparisons <- list(c("Tumor_plus_WT_NK_timepoint_2", "Tumor_only_timepoint_2"),
#                             c("Tumor_plus_IFNg_timepoint_2", "Tumor_only_timepoint_2"),
#                             c("Tumor_plus_PrfKO_NK_timepoint_2", "Tumor_only_timepoint_2"),
#                             c("Tumor_plus_WT_NK_timepoint_2", "Tumor_plus_IFNg_timepoint_2"),
#                             c("Tumor_plus_WT_NK_timepoint_2", "Tumor_plus_PrfKO_NK_timepoint_2"),
#                             c("Tumor_only_timepoint_0", "Tumor_only_timepoint_1"),
#                             c("Tumor_only_timepoint_0", "Tumor_only_timepoint_2"),
#                             c("Tumor_only_timepoint_1", "Tumor_only_timepoint_2"),
#                             c("Tumor_plus_WT_NK_timepoint_2", "Tumor_only_timepoint_1"))

list_of_comparisons <- list(c("Tumor_plus_WT_NK_timepoint_2", "Tumor_only_timepoint_2"),
                            c("Tumor_plus_IFNg_timepoint_2", "Tumor_only_timepoint_2"),
                            c("Tumor_plus_PrfKO_NK_timepoint_2", "Tumor_only_timepoint_2"))

experiment_name="nk_tum_immunoedit_complete"
output_dir <- "/home/rstudio/workspace/results_dir/nk_tum_immunoedit_complete/"

# Loading the data
# raw and filtered dds
precomputed_objects_filename <- paste0(experiment_name, "_dds_objects.RData") #"nk_tum_immunoedit_complete_dds_objects.RData" 
precomputed_objects_file <- file.path(output_dir, precomputed_objects_filename)
# log2 and vsd transformed
precomputed_transf_objects_filename <- "nk_tum_immunoedit_complete_log2_vsd_filt_objects.RData"
precomputed_transf_objects_file <- file.path(output_dir, precomputed_transf_objects_filename)
# check if object loaded if not load
if(!exists(precomputed_objects_file)){
  load(precomputed_objects_file)
  load(precomputed_transf_objects_file)
} 

# defining parameters
abs_filt_samples=3
padj_cutoff = 0.05
log2FC_cutoff = 0.58 #(FC=1.5); log2FC=1.0 # (FC=2)
var_expl_needed <- 0.6         # at least 60% variance explained needed

experiment_subset <- c("EXP9.8") #we use only this experiment
if(exists("deg_design")) {rm(deg_design)}
deg_design <- as.formula("~ cell_line_label + condition_tp") #Experiment is missing here, since we have only EXP9.8

# Loading MsigDB geneset collections ----
gs_hallmark <- hypeR::msigdb_gsets(species = "Mus musculus", category = c("H"), clean=TRUE) 
gs_C2_kegg <- hypeR::msigdb_gsets(species = "Mus musculus", category = c("C2"), subcategory = "CP:KEGG", clean=TRUE) 
gs_C5_GOBP <- hypeR::msigdb_gsets(species = "Mus musculus", category = c("C5"), subcategory = "GO:BP", clean=TRUE) 
gs_C5_GOCC <- hypeR::msigdb_gsets(species = "Mus musculus", category = c("C5"), subcategory = "GO:CC", clean=TRUE) 
gs_C5_GOMF <- hypeR::msigdb_gsets(species = "Mus musculus", category = c("C5"), subcategory = "GO:MF", clean=TRUE) 

genes_of_interest <- list("Denn2b"="ENSMUSG00000031024",
                          "Gbp6"="ENSMUSG00000104713",
                          "H2-K1"="ENSMUSG00000061232",
                          "Hs3st2"="ENSMUSG00000046321",
                          "Htra3"="ENSMUSG00000029096",
                          "Ifi27"="ENSMUSG00000064215",
                          "Igtp"="ENSMUSG00000078853",
                          "Irf1"="ENSMUSG00000018899",
                          "Lgals3bp"="ENSMUSG00000033880",
                          "Ly6a"="ENSMUSG00000075602",
                          "Plaat3"="ENSMUSG00000060675",
                          "Rtp4"="ENSMUSG00000033355",
                          "Serpina3f"="ENSMUSG00000066363",
                          "Stat1"="ENSMUSG00000026104")

genes_of_interest_exp9.8 <- list("H2-Eb1" = "ENSMUSG00000060586",
                                 "Slc16a3"= "ENSMUSG00000025161",
                                 "H2-Aa"  = "ENSMUSG00000036594",
                                 "Bnip3"  = "ENSMUSG00000078566",
                                 "Il1r2"  = "ENSMUSG00000026073",
                                 "Dapk2"  = "ENSMUSG00000032380",
                                 "Zfhx3"  = "ENSMUSG00000038872",
                                 "Dpp7"   = "ENSMUSG00000026958")


#specifically for the publication 
genes_of_interest_exp9.4_9.7 <- list('Ifi44'     ="ENSMUSG00000028037", 
                                     'Ccl5'      ="ENSMUSG00000035042",
                                     'Iigp1'     ="ENSMUSG00000054072",
                                     'Serpina3f' ="ENSMUSG00000066363",
                                     'Ly6a'      ="ENSMUSG00000075602",
                                     'Plaat3'    ="ENSMUSG00000060675",
                                     'Lgals3bp'  ="ENSMUSG00000033880",
                                     'Ifi27'     ="ENSMUSG00000064215",
                                     'Gbp6'      ="ENSMUSG00000104713",
                                     'Rtp4'      ="ENSMUSG00000033355",
                                     'Stat1'     ="ENSMUSG00000026104",
                                     'Tap1'      ="ENSMUSG00000037321",
                                     'H2-K1'     ="ENSMUSG00000061232",
                                     'B2m'       ="ENSMUSG00000060802",
                                     'Igtp'      ="ENSMUSG00000078853",
                                     'Gtf2ird1'  ="ENSMUSG00000023079",
                                     'Cdc42ep4'  ="ENSMUSG00000041598",
                                     'Hoxb6'     ="ENSMUSG00000000690",
                                     'Bfsp2'     ="ENSMUSG00000032556",
                                     'Sox6'      ="ENSMUSG00000051910",
                                     'Tgm2'      ="ENSMUSG00000037820",
                                     'Adprhl1'   ="ENSMUSG00000031448")
# this will be a counter 
cid <- 0

#Star of the loop
for (comparison_i in list_of_comparisons){
  cid <- cid + 1
  #determine the name of the comparison
  deg_name <- paste0(cid, comparison_i[1], comparison_i[2]) #"c1_Tumor_plus_WT_NK_timepoint_2_VS_Tumor_only_timepoint_2"
  deg_dir <- file.path(output_dir, paste0("deg_", deg_name, "/"))
  dir.create(deg_dir)
  
  #this is the tow sample that we are going to compare between
  condition_tp_subset <- comparison_i
  
  # preparing dds and vsd objects for the main project comparison
  precomputed_objects_deg_filename <- paste0(deg_name, "_dds_objects.RData")
  precomputed_objects_deg_file <- file.path(deg_dir, precomputed_objects_deg_filename)
  
  precomputed_transf_objects_deg_filename <- paste0(deg_name, "_log2_vsd_filt_objects.RData")
  precomputed_transf_objects_deg_file <- file.path(deg_dir, precomputed_transf_objects_deg_filename)
  
  # just in case remove any existing subsets
  if(exists("dds_subset")) {rm(dds_subset)}
  if(exists("dds_subset_filt")) {rm(dds_subset_filt)}
  if(exists("vsd_subset")) {rm(vsd_subset)}
  
  if(!file.exists(precomputed_objects_deg_file)){
    
    cat("RNA objects file '", precomputed_objects_deg_filename, "' does not exist in the OUTPUT_DIR...\n")
    cat("generating", precomputed_objects_deg_filename, "\n")
    
    dds_subset <- dds_filt[ , (dds_filt$condition_tp %in% condition_tp_subset) & (dds_filt$experiment %in% experiment_subset)]  
    
    # Dropping levels
    dds_subset$condition_tp <- droplevels(dds_subset$condition_tp)
    table(dds_subset$condition_tp)
    dds_subset$experiment <- droplevels(dds_subset$experiment)
    table(dds_subset$experiment)
    dds_subset$experiment <- droplevels(dds_subset$experiment)
    dds_subset$cell_line_label <- droplevels(dds_subset$cell_line_label)
    
    # filtering lowly expressed genes
    dds_subset_filt <- filterDatasets(dds_subset, 
                                      abs_filt = TRUE, 
                                      abs_filt_samples = abs_filt_samples) # at least in N samples, which is a smallest group size
    
    design(dds_subset_filt) <- deg_design
    dds_subset_filt <- DESeq2::estimateSizeFactors(dds_subset_filt)
    dds_subset_filt <- DESeq2::DESeq(dds_subset_filt) # do not replace outliers based on replicates
    
    cat("saving...", precomputed_objects_file, "\n")
    cat("...into:", output_dir, "\n")
    save(dds_subset, dds_subset_filt, ensemblAnnot,  file = precomputed_objects_deg_file)
    
  } else{
    cat("RNA objects file '", precomputed_objects_deg_filename, "' exist...loading\n")
    load(precomputed_objects_deg_file)
  }
  
  #transformation after filtering
  if(!file.exists(precomputed_transf_objects_deg_file)){
    # transformation
    log2_norm_subset_filt <- DESeq2::normTransform(dds_subset_filt)
    vsd_subset_filt <- DESeq2::vst(dds_subset_filt, blind = FALSE) # using blind=FALSE utilize design info; blind = TRUE for QC
    save(log2_norm_subset_filt, vsd_subset_filt,   
         file = precomputed_transf_objects_deg_file)
  } else{
    load(precomputed_transf_objects_deg_file)
  }
  
  rm(key_metadata_subset, key_variables_tableOne) # in case this exists from previous runs
  key_metadata_subset <- as.data.frame(colData(dds_subset)) %>%
                         dplyr::select(experiment, condition_tp, cell_line_label, technical_replicate) 
  
  key_variables_tableOne <- tableone::CreateTableOne(vars = colnames(dplyr::select(key_metadata_subset, -condition_tp)), 
                                                     strata = c("condition_tp"), 
                                                     data = key_metadata_subset)
  
  tableone::kableone(key_variables_tableOne$CatTable,
                     caption = "Overview of number of samples in different categories (experiment, condition,...).") %>%
                    kableExtra::kable_material(c("striped", "hover"))
  
  transf_object <- vsd_subset_filt
  
  #Before removing Batch Effect
  pca_deg_plotCellLabel <- generatePCA(transf_object = transf_object, 
                                       cond_interest_varPart = c("condition_tp", "cell_line_label", "experiment"), 
                                       color_variable = "condition_tp", 
                                       shape_variable = "cell_line_label",
                                       ntop_genes = 1000) +
                            ggtitle(paste(comparison_i, "Comparison. Dataset Before Batch Correction"))
  #plot(pca_deg_plotCellLabel)
  
  # Remove batch effect (only for the cell_line_label)
  transf_batch_NObatch_cell_label <- vsd_subset_filt
  transf_batch_NObatch_cell_label_count <- limma::removeBatchEffect(SummarizedExperiment::assay(transf_batch_NObatch_cell_label), 
                                                                    transf_batch_NObatch_cell_label$cell_line_label)
  SummarizedExperiment::assay(transf_batch_NObatch_cell_label) <- transf_batch_NObatch_cell_label_count
  
  pca_deg_NObatch_cell_label <- generatePCA(transf_object = transf_batch_NObatch_cell_label, 
                                            cond_interest_varPart = c("condition_tp", "cell_line_label"), 
                                            color_variable = "condition_tp", 
                                            shape_variable = "cell_line_label",
                                            ntop_genes = 1000) +
                            ggtitle(paste(comparison_i, "Comparison. Removed batchEffect - cell_line"))
  #plot(pca_deg_NObatch_cell_label)
  
  PCA_batch_highlightCellLines_merged <- ggpubr::ggarrange(pca_deg_plotCellLabel, 
                                                           pca_deg_NObatch_cell_label, 
                                                           common.legend = TRUE)
  
  ggsave(filename = paste0(deg_dir, comparison_i[1], "_VS_",comparison_i[2], "batch_correction.png"), 
         plot=PCA_batch_highlightCellLines_merged,
         width = 40, height = 20, units = "cm")

  
  #Generate results
  deg_comparison_results <- generateResults_upd(dds_object = dds_subset_filt,
                                                          coeff_name = resultsNames(dds_subset_filt)[3],
                                                          cond_numerator = comparison_i[1],
                                                          cond_denominator = comparison_i[2],
                                                          cond_variable="condition_tp")
  #export it as xlsx table
  openxlsx::write.xlsx(deg_comparison_results, file = file.path(deg_dir, paste0("deg", comparison_i[1], '_vs_', comparison_i[2], "_results.xlsx")))

  #generating GOBP GOCC GOMF
  deg_comparison_results_enrichment_C5_GOBP <- hypeR::hypeR(signature = deg_comparison_results$results_signif$mgi_symbol,
                                                            genesets = gs_C5_GOBP,
                                                            test="hypergeometric",
                                                            background=nrow(dds_subset_filt))
  deg_comparison_results_enrichment_C5_GOCC <- hypeR::hypeR(signature = deg_comparison_results$results_signif$mgi_symbol,
                                                            genesets = gs_C5_GOCC,
                                                            test="hypergeometric",
                                                            background=nrow(dds_subset_filt))
  deg_comparison_results_enrichment_C5_GOMF <- hypeR::hypeR(signature = deg_comparison_results$results_signif$mgi_symbol,
                                                            genesets = gs_C5_GOMF,
                                                            test="hypergeometric",
                                                            background=nrow(dds_subset_filt))

  # hypeR::hyp_show(hyp_obj$data$clA, simple = FALSE)
  hypeR::hyp_to_excel(deg_comparison_results_enrichment_C5_GOBP, file_path=file.path(deg_dir, paste0(comparison_i[1], comparison_i[2],"_enrichment_C5_GOBP.xlsx")))
  hypeR::hyp_to_excel(deg_comparison_results_enrichment_C5_GOCC, file_path=file.path(deg_dir, paste0(comparison_i[1], comparison_i[2],"_enrichment_C5_GOCC.xlsx")))
  hypeR::hyp_to_excel(deg_comparison_results_enrichment_C5_GOMF, file_path=file.path(deg_dir, paste0(comparison_i[1], comparison_i[2],"_enrichment_C5_GOMF.xlsx")))
  # plotting enrichment results ----
  deg_comparison_results_enrichment_C5_GOBP_plot <- hypeR::hyp_dots(deg_comparison_results_enrichment_C5_GOBP, merge=TRUE, fdr=0.05, top = 20, abrv=70, val="fdr", title=paste0("GOBP: ",comparison_i[1], comparison_i[2]))
  deg_comparison_results_enrichment_C5_GOCC_plot <- hypeR::hyp_dots(deg_comparison_results_enrichment_C5_GOCC, merge=TRUE, fdr=0.05, top = 20, abrv=70, val="fdr", title=paste0("GOCC: ",comparison_i[1], comparison_i[2]))
  deg_comparison_results_enrichment_C5_GOMF_plot <- hypeR::hyp_dots(deg_comparison_results_enrichment_C5_GOMF, merge=TRUE, fdr=0.05, top = 20, abrv=70, val="fdr", title=paste0("GOMF: ",comparison_i[1], comparison_i[2]))

  ggsave(filename = paste0(deg_dir, comparison_i[1], "_VS_",comparison_i[2], "_GOBP_plot.png"),
         plot=deg_comparison_results_enrichment_C5_GOBP_plot,
         width = 20, height = 20, units = "cm")

  ggsave(filename = paste0(deg_dir, comparison_i[1], "_VS_",comparison_i[2], "_GOCC_plot.png"),
         plot=deg_comparison_results_enrichment_C5_GOCC_plot,
         width = 20, height = 20, units = "cm")

  ggsave(filename = paste0(deg_dir, comparison_i[1], "_VS_",comparison_i[2], "_GOMF_plot.png"),
         plot=deg_comparison_results_enrichment_C5_GOMF_plot,
         width = 20, height = 20, units = "cm")
  
  # volcano plot
  deg_comparison_results_volcano_plot <- plotVolcano_repel(dds_results_obj = deg_comparison_results$results_all,
                                                    genes_of_interest = genes_of_interest,
                                                    genes_of_interest2 = genes_of_interest_exp9.8,
                                                    plot_title = paste0(comparison_i[1], ' vs ', comparison_i[2]),
                                                    max_overlaps = 30)

  ggsave(filename = paste0(deg_dir, comparison_i[1], "_VS_",comparison_i[2], "_volcano_plot.png"),
         plot=deg_comparison_results_volcano_plot,
         width = 20, height = 20, units = "cm")

  ggsave(filename = paste0(deg_dir, comparison_i[1], "_VS_",comparison_i[2], "_volcano_plot.eps"),
         plot=deg_comparison_results_volcano_plot,
         width = 20, height = 20, units = "cm")
  
  print(deg_comparison_results_volcano_plot)

  # heatmap and heatmap batch corrected - corrected
  
  save_pheatmap_pdf <- function(x, filename, width=7, height=7) {
    stopifnot(!missing(x))
    stopifnot(!missing(filename))
    pdf(filename, width=width, height=height)
    grid::grid.newpage()
    grid::grid.draw(x$gtable)
    dev.off()
  }
  
  metadata_heatmap <- as.data.frame(colData(dds_subset))
  heatmap_counts <- SummarizedExperiment::assay(transf_object)
  heatmap_counts_NObatch <- SummarizedExperiment::assay(transf_batch_NObatch_cell_label) # removed xperiment batch effects! ? use experimen+cell_line removed???
  heatmap_counts_deg <- heatmap_counts[rownames(heatmap_counts) %in% deg_comparison_results$results_signif$ensembl_id, ]
  heatmap_counts_NObatch_deg <- heatmap_counts_NObatch[rownames(heatmap_counts_NObatch) %in% deg_comparison_results$results_signif$ensembl_id, ]
  annotation_col <- metadata_heatmap %>%
    dplyr::select(cell_line_label, condition_tp) %>% # condition, timepoint_cell_harvesting,
    dplyr::arrange(condition_tp, cell_line_label)
  heatmap_counts_deg_ord <- heatmap_counts_deg[, match(rownames(annotation_col), colnames(heatmap_counts_deg))]
  heatmap_counts_NObatch_deg_ord <- heatmap_counts_NObatch_deg[, match(rownames(annotation_col), colnames(heatmap_counts_NObatch_deg))]
  ensembl2symbol_annot <- deg_comparison_results$results_all %>%
    dplyr::select(ensembl_id, mgi_symbol)

 

  color.scheme <- rev(RColorBrewer::brewer.pal(8,"RdBu")) # generate the color scheme to use

  ann_colors = list(
    experiment = c(EXP9.8 = "grey")
  )

  # ,                  filename = paste0(deg_TpNK_tp2_vs_Tonly_tp1_DIR, "tp2_vs_tp1_inclEXP9_7_heatmap_intersect_54genes.pdf")
  heatmap_result <- pheatmap::pheatmap(heatmap_counts_NObatch_deg_ord,
                                       main = "Heatmap of signif. DEG - removed cell line BatchEffect",
                                       scale = "row",
                                       annotation_col = annotation_col,
                                       annotation_colors = ann_colors,
                                       #annotation_row = row_annot_symbols,
                                       show_colnames = FALSE,
                                       show_rownames = FALSE,
                                       cluster_cols = FALSE,
                                       #cluster_rows = counts_deg_ord_row_cor_hclust,
                                       color = color.scheme,
                                       fontsize = 10, fontsize_row = 10 #height=10, cellwidth = 11, cellheight = 11
  )
  save_pheatmap_pdf(filename = paste0(deg_dir, comparison_i[1], "_VS_",comparison_i[2], "_heatmap_batch_eff_removed.pdf"),
                    x=heatmap_result,
                    width = 10, height = 10)
  
  ggsave(filename = paste0(deg_dir, comparison_i[1], "_VS_",comparison_i[2], "_heatmap_batch_eff_removed.png"),
         plot=heatmap_result,
         width = 10, height = 10, units = "in")

  #Heatmap not corrected for the batch effect
  heatmap_result <- pheatmap::pheatmap(heatmap_counts_deg_ord,
                                       main = "Heatmap of signif. DEG",
                                       scale = "row",
                                       annotation_col = annotation_col,
                                       annotation_colors = ann_colors,
                                       #annotation_row = row_annot_symbols,
                                       show_colnames = FALSE,
                                       show_rownames = FALSE,
                                       cluster_cols = FALSE,
                                       #cluster_rows = counts_deg_ord_row_cor_hclust,
                                       color = color.scheme,
                                       fontsize = 10, fontsize_row = 10 #height=10, cellwidth = 11, cellheight = 11
  )
  save_pheatmap_pdf(filename = paste0(deg_dir, comparison_i[1], "_VS_",comparison_i[2], "_heatmap.pdf"),
                    x=heatmap_result,
                    width = 10, height = 10)
  
  ggsave(filename = paste0(deg_dir, comparison_i[1], "_VS_",comparison_i[2], "_heatmap.png"),
         plot=heatmap_result,
         width = 10, height = 10, units = "in")
  
}                            
## RNA objects file ' 1Tumor_plus_WT_NK_timepoint_2Tumor_only_timepoint_2_dds_objects.RData ' exist...loading

## RNA objects file ' 2Tumor_plus_IFNg_timepoint_2Tumor_only_timepoint_2_dds_objects.RData ' exist...loading

## RNA objects file ' 3Tumor_plus_PrfKO_NK_timepoint_2Tumor_only_timepoint_2_dds_objects.RData ' exist...loading

Plotting the Venn Diagramm for the comparisons:

| Deg | Group 1 | Group 2 |

| — | ——————————- | ——————————- |

| 1 | Tumor_plus_WT_NK_timepoint_2 | Tumor_only_timepoint_2 |

| 2 | Tumor_plus_IFNg_timepoint_2 | Tumor_only_timepoint_2 |

| 3 | Tumor_plus_PrfKO_NK_timepoint_2 | Tumor_only_timepoint_2 |

Deg1_filename <-  paste0("~/workspace/results_dir/nk_tum_immunoedit_complete/deg_1Tumor_plus_WT_NK_timepoint_2Tumor_only_timepoint_2/degTumor_plus_WT_NK_timepoint_2_vs_Tumor_only_timepoint_2_results.xlsx")
Deg2_filename <-  paste0("~/workspace/results_dir/nk_tum_immunoedit_complete/deg_2Tumor_plus_IFNg_timepoint_2Tumor_only_timepoint_2/degTumor_plus_IFNg_timepoint_2_vs_Tumor_only_timepoint_2_results.xlsx")
Deg3_filename <-  paste0("~/workspace/results_dir/nk_tum_immunoedit_complete/deg_3Tumor_plus_PrfKO_NK_timepoint_2Tumor_only_timepoint_2/degTumor_plus_PrfKO_NK_timepoint_2_vs_Tumor_only_timepoint_2_results.xlsx")

Deg1 <- openxlsx::read.xlsx(Deg1_filename,sheet = 'results_signif')
Deg1_ens <- Deg1[,c('mgi_symbol', 'ensembl_id')]
Deg1 <- Deg1$mgi_symbol

Deg2 <- openxlsx::read.xlsx(Deg2_filename,sheet = 'results_signif')
Deg2_ens <- Deg2[, c('mgi_symbol', 'ensembl_id')]
Deg2 <- Deg2$mgi_symbol

Deg3 <- openxlsx::read.xlsx(Deg3_filename,sheet = 'results_signif')
Deg3_ens <- Deg3[, c('mgi_symbol', 'ensembl_id')]
Deg3 <- Deg3$mgi_symbol

Deg_list <-  list("Tmr+WT_NK TP2 vs T only TP2" = Deg1,
                  "Tmr+IFNg TP2 vs T only TP2" = Deg2, 
                  "Tmr+PrfKO NK TP2 vs T only TP2" = Deg3)

library(ggvenn)

ven_diag <- ggvenn(Deg_list)
ven_diag

#order the genes for the further Heatmap
Deg12_intersect <- intersect(Deg1, Deg2)
Deg123_intersect <- intersect(Deg12_intersect, Deg3)
Deg23_intersect <- intersect(Deg2, Deg3)
Deg31_intersect <- intersect(Deg3, Deg1)

Deg12_intersect <- Deg12_intersect[!(Deg12_intersect %in% Deg123_intersect)]
Deg23_intersect <- Deg23_intersect[!(Deg23_intersect %in% Deg123_intersect)]
Deg31_intersect <- Deg31_intersect[!(Deg31_intersect %in% Deg123_intersect)]

Deg1_unique <- Deg1[!(Deg1 %in% Deg12_intersect | 
                      Deg1 %in% Deg31_intersect |
                      Deg1 %in% Deg123_intersect)]

Deg2_unique <- Deg2[!(Deg2 %in% Deg12_intersect |
                      Deg2 %in% Deg23_intersect |
                      Deg2 %in% Deg123_intersect)]

Deg3_unique <- Deg3[!(Deg3 %in% Deg31_intersect |
                      Deg3 %in% Deg23_intersect |
                      Deg3 %in% Deg123_intersect)]

List_of_genes_ordered <- c(Deg31_intersect,
                           Deg1_unique,
                           Deg12_intersect,
                           Deg2_unique,
                           Deg23_intersect,
                           Deg3_unique,
                           Deg123_intersect)

Deg_ens <- rbind(Deg1_ens, Deg2_ens, Deg3_ens)
Deg_ens <- unique(Deg_ens)

Deg_ens_ord <- Deg_ens[match(List_of_genes_ordered, Deg_ens$mgi_symbol),]
#These are the data that will be used for the heatmap

list_of_comparisons_venn_heatmap <- c("Tumor_plus_WT_NK_timepoint_2", 
                                      "Tumor_plus_IFNg_timepoint_2", 
                                      "Tumor_plus_PrfKO_NK_timepoint_2", 
                                      "Tumor_only_timepoint_2")
experiment_subset <- c("EXP9.8")

dds_subset_venn_heatmap <- dds_filt[ , (dds_filt$condition_tp %in% list_of_comparisons_venn_heatmap) & (dds_filt$experiment %in% experiment_subset)]  
metadata_heatmap <- as.data.frame(colData(dds_subset_venn_heatmap))
heatmap_counts <- SummarizedExperiment::assay(dds_subset_venn_heatmap)
heatmap_counts_deg <- heatmap_counts[rownames(heatmap_counts) %in% Deg_ens_ord$ensembl_id,]
annotation_col <- metadata_heatmap %>%
  dplyr::select(cell_line_label, condition_tp) %>% # condition, timepoint_cell_harvesting,
  dplyr::arrange(condition_tp, cell_line_label)
heatmap_counts_deg_ord <- heatmap_counts_deg[, match(rownames(annotation_col), colnames(heatmap_counts_deg))]
heatmap_counts_deg_ord <- heatmap_counts_deg_ord[match(Deg_ens_ord$ensembl_id, rownames(heatmap_counts_deg_ord)),]
rownames(heatmap_counts_deg_ord) == Deg_ens_ord$ensembl_id
##   [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
##  [27] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
##  [53] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
##  [79] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [105] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [131] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [157] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [183] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [209] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [235] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [261] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [287] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [313] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [339] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [365] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [391] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [417] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [443] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [469] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [495] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [521] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [547] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [573] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [599] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [625] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [651] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [677] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [703] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [729] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
rownames(heatmap_counts_deg_ord) <- Deg_ens_ord$mgi_symbol

color.scheme <- rev(RColorBrewer::brewer.pal(8,"RdBu")) # generate the color scheme to use
ann_colors = list(
  experiment = c(EXP9.4 = "#4682B4", EXP9.5 = "#7846B4", EXP9.6 = "#B47846", EXP9.7 = "#82B446", EXP9.8 = "grey")
)

heatmap_result <- pheatmap::pheatmap(heatmap_counts_deg_ord,
                                     main = "Heatmap of signif. DEG",
                                     scale = "row",
                                     annotation_col = annotation_col,
                                     annotation_colors = ann_colors,
                                     #annotation_row = row_annot_symbols,
                                     show_colnames = FALSE,
                                     show_rownames = TRUE,
                                     cluster_cols = FALSE,
                                     cluster_rows = T,
                                     annotation_names_row = TRUE,
                                     #cluster_rows = counts_deg_ord_row_cor_hclust,
                                     color = color.scheme,
                                     fontsize = 10, fontsize_row = 10 #height=10, cellwidth = 11, cellheight = 11
)

# This heatmap doesn't look so great.

# save the DEG list:
List_of_genes_ordered_forxlsx <- list("Deg1_T+WT_NK_TP2_VS_To_TP2" = Deg1,
                                      "Deg2_T+IFNg_TP2_VS_To_TP2" = Deg2,
                                      "Deg3_T+PrfKO_NK_TP2_VS_To_TP2" = Deg3,
                                      "Deg31_exlusive_intersect" = Deg31_intersect,
                                      "Deg1_unique" = Deg1_unique,
                                      "Deg12_exlusive_intersect" = Deg12_intersect,
                                      "Deg2_unique" = Deg2_unique,
                                      "Deg23_exlusive_intersect" = Deg23_intersect,
                                      "Deg3_unique" = Deg3_unique,
                                      "Deg123_intersect" = Deg123_intersect)

openxlsx::write.xlsx(List_of_genes_ordered_forxlsx, 
                     "~/workspace/results_dir/nk_tum_immunoedit_complete/overlaping_genes_deg_1_2_3.xlsx")

# adding supplementary information (p-val, L2FC, etc for genes)
# read the DEG files again
Deg1_xlsx <- openxlsx::read.xlsx(Deg1_filename,sheet = 'results_signif')
Deg1_xlsx <- Deg1_xlsx %>% select("ensembl_id", "mgi_symbol", "mgi_description",  "log2FoldChange", "padj")

Deg2_xlsx <- openxlsx::read.xlsx(Deg2_filename,sheet = 'results_signif')
Deg2_xlsx <- Deg2_xlsx %>% select("ensembl_id", "mgi_symbol", "mgi_description",   "log2FoldChange",  "padj")

Deg3_xlsx <- openxlsx::read.xlsx(Deg3_filename,sheet = 'results_signif')
Deg3_xlsx <- Deg3_xlsx %>% select("ensembl_id", "mgi_symbol", "mgi_description",  "log2FoldChange", "padj")


List_of_genes_ordered_forxlsx_sup <- List_of_genes_ordered_forxlsx

List_of_genes_ordered_forxlsx_sup$`Deg1_T+WT_NK_TP2_VS_To_TP2` <- Deg1_xlsx[match(List_of_genes_ordered_forxlsx_sup$`Deg1_T+WT_NK_TP2_VS_To_TP2`, Deg1_xlsx$mgi_symbol),]
List_of_genes_ordered_forxlsx_sup$`Deg2_T+IFNg_TP2_VS_To_TP2` <- Deg2_xlsx[match(List_of_genes_ordered_forxlsx_sup$`Deg2_T+IFNg_TP2_VS_To_TP2`, Deg2_xlsx$mgi_symbol),]
List_of_genes_ordered_forxlsx_sup$`Deg3_T+PrfKO_NK_TP2_VS_To_TP2` <- Deg3_xlsx[match(List_of_genes_ordered_forxlsx_sup$`Deg3_T+PrfKO_NK_TP2_VS_To_TP2`, Deg3_xlsx$mgi_symbol),]

List_of_genes_ordered_forxlsx_sup$Deg1_unique <-  Deg1_xlsx[match(List_of_genes_ordered_forxlsx_sup$Deg1_unique, Deg1_xlsx$mgi_symbol),]
List_of_genes_ordered_forxlsx_sup$Deg2_unique <-  Deg2_xlsx[match(List_of_genes_ordered_forxlsx_sup$Deg2_unique, Deg2_xlsx$mgi_symbol),]
List_of_genes_ordered_forxlsx_sup$Deg3_unique <-  Deg3_xlsx[match(List_of_genes_ordered_forxlsx_sup$Deg3_unique, Deg3_xlsx$mgi_symbol),]

Deg31_excl <- Deg3_xlsx[match(List_of_genes_ordered_forxlsx_sup$Deg31_exlusive_intersect, Deg3_xlsx$mgi_symbol),] %>%
              rename("DEG3_log2FoldChange" = "log2FoldChange", "DEG3_padj" = "padj")
Deg13_excl <- Deg1_xlsx[match(List_of_genes_ordered_forxlsx_sup$Deg31_exlusive_intersect, Deg1_xlsx$mgi_symbol),] %>%
  select("log2FoldChange", "padj") %>%
  rename("DEG1_log2FoldChange" = "log2FoldChange", "DEG1_padj" = "padj")
List_of_genes_ordered_forxlsx_sup$Deg31_exlusive_intersect <- cbind(Deg31_excl, Deg13_excl)

Deg21_excl <- Deg2_xlsx[match(List_of_genes_ordered_forxlsx_sup$Deg12_exlusive_intersect, Deg2_xlsx$mgi_symbol),] %>%
  rename("DEG2_log2FoldChange" = "log2FoldChange", "DEG2_padj" = "padj")
Deg12_excl <- Deg1_xlsx[match(List_of_genes_ordered_forxlsx_sup$Deg12_exlusive_intersect, Deg1_xlsx$mgi_symbol),] %>%
  select("log2FoldChange", "padj") %>%
  rename("DEG1_log2FoldChange" = "log2FoldChange", "DEG1_padj" = "padj")
List_of_genes_ordered_forxlsx_sup$Deg12_exlusive_intersect <- cbind(Deg21_excl, Deg12_excl)

Deg23_excl <- Deg2_xlsx[match(List_of_genes_ordered_forxlsx_sup$Deg23_exlusive_intersect, Deg2_xlsx$mgi_symbol),] %>%
  rename("DEG2_log2FoldChange" = "log2FoldChange", "DEG2_padj" = "padj")
Deg32_excl <- Deg3_xlsx[match(List_of_genes_ordered_forxlsx_sup$Deg23_exlusive_intersect, Deg3_xlsx$mgi_symbol),] %>%
  select("log2FoldChange", "padj") %>%
  rename("DEG3_log2FoldChange" = "log2FoldChange", "DEG3_padj" = "padj")
List_of_genes_ordered_forxlsx_sup$Deg12_exlusive_intersect <- cbind(Deg23_excl, Deg32_excl)


Deg123_1 <- Deg1_xlsx[match(List_of_genes_ordered_forxlsx_sup$Deg123_intersect, Deg1_xlsx$mgi_symbol),] %>%
  rename("DEG1_log2FoldChange" = "log2FoldChange", "DEG1_padj" = "padj")

Deg123_2 <- Deg2_xlsx[match(List_of_genes_ordered_forxlsx_sup$Deg123_intersect, Deg2_xlsx$mgi_symbol),] %>%
  select("log2FoldChange", "padj") %>%
  rename("DEG2_log2FoldChange" = "log2FoldChange", "DEG2_padj" = "padj")

Deg123_3 <- Deg3_xlsx[match(List_of_genes_ordered_forxlsx_sup$Deg123_intersect, Deg3_xlsx$mgi_symbol),] %>%
  select("log2FoldChange", "padj") %>%
  rename("DEG3_log2FoldChange" = "log2FoldChange", "DEG3_padj" = "padj")

List_of_genes_ordered_forxlsx_sup$Deg123_intersect <- cbind(Deg123_1, Deg123_2, Deg123_3)


openxlsx::write.xlsx(List_of_genes_ordered_forxlsx_sup, 
                     "~/workspace/results_dir/nk_tum_immunoedit_complete/overlaping_genes_deg_1_2_3_with_extra.xlsx")
Plots for the publication:

Plotting the PCA plot of the 4 groups

#Tumor_plus_WT_NK_timepoint_2 Tumor_plus_IFNg_timepoint_2 Tumor_plus_PrfKO_NK_timepoint_2 Tumor_only_timepoint_2

save_dir <- "~/workspace/results_dir/EXP9.8_publication/"
dir.create(save_dir)

condition_tp_subset <- c("Tumor_plus_WT_NK_timepoint_2",  
                         "Tumor_plus_IFNg_timepoint_2", 
                         "Tumor_plus_PrfKO_NK_timepoint_2", 
                         "Tumor_only_timepoint_2")

dds_subset <- dds_filt[ , (dds_filt$condition_tp %in% condition_tp_subset) & (dds_filt$experiment %in% experiment_subset)]  

# Dropping levels
dds_subset$condition_tp <- droplevels(dds_subset$condition_tp)
table(dds_subset$condition_tp)
## 
##          Tumor_only_timepoint_2    Tumor_plus_WT_NK_timepoint_2     Tumor_plus_IFNg_timepoint_2 Tumor_plus_PrfKO_NK_timepoint_2 
##                               6                               6                               6                               6
dds_subset$experiment <- droplevels(dds_subset$experiment)
table(dds_subset$experiment)
## 
## EXP9.8 
##     24
dds_subset$experiment <- droplevels(dds_subset$experiment)
dds_subset$cell_line_label <- droplevels(dds_subset$cell_line_label)

# filtering lowly expressed genes
dds_subset_filt <- filterDatasets(dds_subset, 
                                  abs_filt = TRUE, 
                                  abs_filt_samples = abs_filt_samples) # at least in N samples, which is a smallest group size
## Original dds object samples:  24  genes:  15430 
## Minimum number of samples with expression: 3 
## Number of filtered genes: 3228 
## Filtered dds object has samples: 24 genes: 12202
design(dds_subset_filt) <- deg_design
dds_subset_filt <- DESeq2::estimateSizeFactors(dds_subset_filt)
dds_subset_filt <- DESeq2::DESeq(dds_subset_filt) # do not replace outliers based on replicates
log2_norm_subset_filt <- DESeq2::normTransform(dds_subset_filt)
vsd_subset_filt <- DESeq2::vst(dds_subset_filt, blind = FALSE) # using blind=FALSE utilize design info;
rm(key_metadata_subset, key_variables_tableOne) # in case this exists from previous runs
key_metadata_subset <- as.data.frame(colData(dds_subset)) %>%
  dplyr::select(experiment, condition_tp, cell_line_label, technical_replicate) 

key_variables_tableOne <- tableone::CreateTableOne(vars = colnames(dplyr::select(key_metadata_subset, -condition_tp)), 
                                                   strata = c("condition_tp"), 
                                                   data = key_metadata_subset)

tableone::kableone(key_variables_tableOne$CatTable,
                   caption = "Overview of number of samples in different categories (experiment, condition,...).") %>%
  kableExtra::kable_material(c("striped", "hover"))
Overview of number of samples in different categories (experiment, condition,…).
Tumor_only_timepoint_2 Tumor_plus_WT_NK_timepoint_2 Tumor_plus_IFNg_timepoint_2 Tumor_plus_PrfKO_NK_timepoint_2 p test
n 6 6 6 6
experiment = EXP9.8 (%) 6 (100.0) 6 (100.0) 6 (100.0) 6 (100.0) NA
cell_line_label = D (%) 3 ( 50.0) 3 ( 50.0) 3 ( 50.0) 3 ( 50.0) 1.000
technical_replicate (%) 1.000
1 2 ( 33.3) 2 ( 33.3) 2 ( 33.3) 2 ( 33.3)
2 2 ( 33.3) 2 ( 33.3) 2 ( 33.3) 2 ( 33.3)
3 2 ( 33.3) 2 ( 33.3) 2 ( 33.3) 2 ( 33.3)
transf_object <- vsd_subset_filt

#Before removing Batch Effect
pca_deg_plotCellLabel <- generatePCA(transf_object = transf_object[,transf_object$cell_line_label == "A"], 
                                     cond_interest_varPart = c("condition_tp", "cell_line_label", "experiment"), 
                                     color_variable = "condition_tp", 
                                     shape_variable = "cell_line_label",
                                     ntop_genes = 1000) +
  ggtitle(paste( "PCA plot. EXP9.8 Cell line A")) +
  scale_color_manual(values = c("#DD3344" ,"#553388", "#A3E7FC", "#26C485"))
plot(pca_deg_plotCellLabel)

ggsave(filename = file.path(save_dir, "PCA_Plot_all_conditions_A.png"), pca_deg_plotCellLabel,
                            width = 20, height = 14, units = "cm")

pca_deg_plotCellLabel <- generatePCA(transf_object = transf_object[,transf_object$cell_line_label == "D"], 
                                     cond_interest_varPart = c("condition_tp", "cell_line_label", "experiment"), 
                                     color_variable = "condition_tp", 
                                     shape_variable = "cell_line_label",
                                     ntop_genes = 1000) +
  ggtitle(paste( "PCA plot. EXP9.8 Cell line D")) +
  scale_color_manual(values = c("#DD3344" ,"#553388", "#A3E7FC", "#26C485"))+
  scale_shape_manual(values = 17)
plot(pca_deg_plotCellLabel)

ggsave(filename = file.path(save_dir, "PCA_Plot_all_conditions_D.png"), pca_deg_plotCellLabel,
                            width = 20, height = 14, units = "cm")

# Remove batch effect (only for the cell_line_label)
transf_batch_NObatch_cell_label <- vsd_subset_filt
transf_batch_NObatch_cell_label_count <- limma::removeBatchEffect(SummarizedExperiment::assay(transf_batch_NObatch_cell_label), 
                                                                  transf_batch_NObatch_cell_label$cell_line_label)
SummarizedExperiment::assay(transf_batch_NObatch_cell_label) <- transf_batch_NObatch_cell_label_count

pca_deg_NObatch_cell_label <- generatePCA(transf_object = transf_batch_NObatch_cell_label, 
                                          cond_interest_varPart = c("condition_tp", "cell_line_label"), 
                                          color_variable = "condition_tp", 
                                          shape_variable = "cell_line_label",
                                          ntop_genes = 1000) +
  scale_color_manual(values = c("#DD3344" ,"#553388", "#A3E7FC", "#26C485"))+
  ggtitle(paste( "PCA plot. EXP9.8 Batch corrected for cell_line"))
plot(pca_deg_NObatch_cell_label)

ggsave(filename = file.path(save_dir, "PCA_Plot_all_conditions_batch_corrected_cell_lines.png"), pca_deg_NObatch_cell_label,
                            width = 20, height = 14, units = "cm")


##### Venn diagramms

Deg1_filename <-  paste0("~/workspace/results_dir/nk_tum_immunoedit_complete/deg_1Tumor_plus_WT_NK_timepoint_2Tumor_only_timepoint_2/degTumor_plus_WT_NK_timepoint_2_vs_Tumor_only_timepoint_2_results.xlsx")
Deg2_filename <-  paste0("~/workspace/results_dir/nk_tum_immunoedit_complete/deg_2Tumor_plus_IFNg_timepoint_2Tumor_only_timepoint_2/degTumor_plus_IFNg_timepoint_2_vs_Tumor_only_timepoint_2_results.xlsx")
Deg3_filename <-  paste0("~/workspace/results_dir/nk_tum_immunoedit_complete/deg_3Tumor_plus_PrfKO_NK_timepoint_2Tumor_only_timepoint_2/degTumor_plus_PrfKO_NK_timepoint_2_vs_Tumor_only_timepoint_2_results.xlsx")



Deg1 <- openxlsx::read.xlsx(Deg1_filename,sheet = 'results_signif')
Deg1_pos <- Deg1[Deg1$log2FoldChange > 0, c('mgi_symbol')  ]
Deg1_neg <- Deg1[Deg1$log2FoldChange < 0, c('mgi_symbol')  ]

Deg2 <- openxlsx::read.xlsx(Deg2_filename,sheet = 'results_signif')
Deg2_pos <- Deg2[Deg2$log2FoldChange > 0, c('mgi_symbol')  ]
Deg2_neg <- Deg2[Deg2$log2FoldChange < 0, c('mgi_symbol')  ]
Deg2_neg <- Deg2_neg[Deg2_neg!=""]

Deg3 <- openxlsx::read.xlsx(Deg3_filename,sheet = 'results_signif')
Deg3_pos <- Deg3[Deg3$log2FoldChange > 0, c('mgi_symbol')  ]
Deg3_neg <- Deg3[Deg3$log2FoldChange < 0, c('mgi_symbol')  ]
Deg3_neg <- Deg3_neg[Deg3_neg!=""]

Deg_list_pos <-  list("Tmr+WT_NK TP2 vs T only TP2" = Deg1_pos,
                  "Tmr+IFNg TP2 vs T only TP2" = Deg2_pos, 
                  "Tmr+PrfKO NK TP2 vs T only TP2" = Deg3_pos)

Deg_list_neg <-  list("Tmr+WT_NK TP2 vs T only TP2" = Deg1_neg,
                      "Tmr+IFNg TP2 vs T only TP2" = Deg2_neg, 
                      "Tmr+PrfKO NK TP2 vs T only TP2" = Deg3_neg)
library(ggvenn)

ven_diag <- ggvenn(Deg_list_pos,fill_color = c("#553388", "#A3E7FC", "#26C485"))
ven_diag

ggsave(filename = file.path(save_dir, "venn_diag_positive_up_reg.png"), ven_diag,
       width = 20, height = 20, units = "cm")

ven_diag <- ggvenn(Deg_list_neg,fill_color = c("#553388", "#A3E7FC", "#26C485"))
ven_diag

ggsave(filename = file.path(save_dir, "venn_diag_positive_down_reg.png"), ven_diag,
       width = 20, height = 20, units = "cm")


Deg12_intersect_pos <- intersect(Deg1_pos, Deg2_pos)
Deg123_intersect_pos <- intersect(Deg12_intersect_pos, Deg3_pos)
Deg23_intersect_pos <- intersect(Deg2_pos, Deg3_pos)
Deg31_intersect_pos <- intersect(Deg3_pos, Deg1_pos)

Deg12_intersect_pos_scecif <- setdiff(Deg12_intersect_pos, Deg123_intersect_pos)
Deg23_intersect_pos_specif <- setdiff(Deg23_intersect_pos, Deg123_intersect_pos)
Deg31_intersect_pos_specif <- setdiff(Deg31_intersect_pos, Deg123_intersect_pos)

setdiff(Deg1_pos, c(Deg2_pos, Deg3_pos))
##  [1] "Bnip3"   "Aldoa"   "Pgk1"    "Tpi1"    "Ero1l"   "Pkm"     "Ddit4"   "Pdk1"    "Bnip3l"  "Pgm1"    "Fam162a" "Ulk3"    "Pfkl"   
## [14] "Rasal3"  "Pfkp"    "Ankrd37" "Kdm3a"   "Map4"    "Slc37a4" "Tgm2"    "Trpc4"   "Zfp810"  "Cpne5"   "Cox7a1"  "Egln1"   "Ak4"    
## [27] "Aldoc"   "Hsd11b1" "Zfp62"   "Crlf1"   "Mgarp"   "Ropn1l"  "Cd244a"  "Gstm4"   "Ercc1"   "Izumo4"
setdiff(Deg2_pos, c(Deg1_pos, Deg3_pos))
##   [1] "Ifi47"         "Tgtp2"         "Gbp7"          "Gbp4"          "Gbp5"          "Irgm2"         "Tap1"          "H2-K1"        
##   [9] "Jun"           "Socs1"         "Serpina3f"     "Gm12250"       "Irgm1"         "B2m"           "Tapbpl"        "Batf2"        
##  [17] "Cd1d1"         "9330175E14Rik" "F830016B08Rik" "Psmb8"         "Serpina3g"     "Gbp3"          "Parp11"        "Gm12216"      
##  [25] "Irf9"          "AW112010"      "Psmb10"        "Ppa1"          "Nlrc5"         "Wars"          "Lap3"          "Ifitm3"       
##  [33] "Gm43302"       "Parp9"         "Dgcr6"         "Il18bp"        "Ube2l6"        "Trafd1"        "Tapbp"         "Tmem140"      
##  [41] "Psap"          "Nampt"         "Casp1"         "Trim12c"       "Tfrc"          "Rnf19b"        "Gbp2b"         "Ly6c1"        
##  [49] "Isg20"         "Ctss"          "Coa5"          "Gpx1"          "Slfn2"         "Ifi44"         "Psme1"         "Dtx3l"        
##  [57] "Psmg4"         "Bst2"          "Gimap4"        "P2ry14"        "Ifitm1"        "Isg15"         "Tnfrsf26"      "Ffar2"        
##  [65] "Prm1"          "H2-D1"         "Sp110"         "Gm1966"        "Psme2"         "Trim30a"       "Idnk"          "Mpeg1"        
##  [73] "Pecam1"        "Ifngr2"        "Ccr2"          "Tnfsf10"       "Prodh"         "Gm4486"        "Ifi35"         "Itm2b"        
##  [81] "Csf1"          "Psmb9"         "Samhd1"        "Usp18"         "Ogfr"          "Fas"           "Ifi209"        "Rnf114"       
##  [89] "Ly6c2"         "Oasl2"         "Snx10"         "Cd274"         "Cfp"           "Rsad2"         "Plac8"         "Psme2b"       
##  [97] "Guca1a"        "Klf6"          "Tmem33"        "Erap1"         "H2-T22"        "Ctsc"          "Aida"          "Cask"         
## [105] "Ms4a6c"        "Myof"          "Rmdn3"         "BC051226"      "Cemip2"        "Nod1"          "Gm12840"       "Xaf1"         
## [113] "H2-Q5"         "Tgtp1"         "Phc2"          "Parp12"        "Reps1"         "Oas3"          "Mapk9"         "Rnase6"       
## [121] "Glrx"          "Vwa5a"         "Il2rg"         "Lgals3"        "Ppp2r5b"       "1600014C10Rik" "Ly6d"          "Rsbn1l"       
## [129] "Apol7b"        "Nuak2"         "Dio2"          "Ciita"         "Olfr753-ps1"   "Fcgr3"         "Nmi"           "Snhg15"       
## [137] "Tmtc4"         "Apex2"         "Trim34a"       "Dkk3"          "Gm20627"       "Ica1"          "Prdm1"         "Serpinb6a"    
## [145] "Ifit1"         "Cebpb"         "Tmco4"         "Cep89"         "Ccl9"          "Stat3"         "Gm6545"        "Plscr1"       
## [153] "Mmp13"         "Sema3g"        "H2-DMb2"       "Piwil2"        "Uba7"          "Ccrl2"         "Chpf2"         "Rasa4"        
## [161] "Gda"           "Arhgap25"      "Lax1"          "Ifit1bl1"      "Parp14"        "Irf7"          "Gm49339"       "Selenow"      
## [169] "Tusc1"         "Ryr2"          "Atp8a1"        "Sco1"          "Gm48161"       "Fbn1"          "Rgs1"          "Pon3"         
## [177] "Marchf5"       "Znrd2"         "Srxn1"         "Tspan2"        "Gpr18"         "Tmem50b"       "Adar"          "Gm50255"      
## [185] "Osm"           "Phf11a"        "Gm10076"       "Shb"           "Il12rb1"       "Trim12a"       "H2-T23"        "Igf1"         
## [193] "Pgf"           "Wsb2"          "Ifi203"        "Ifi213"        "Scly"          "Usf1"          "Bcl3"          "Alcam"        
## [201] "Sh2d5"         "Timm21"        "Cd36"          "Vsir"          "Snhg5"         "Cmpk2"         "Minpp1"        "Tmem138"      
## [209] "Traf5"         "Creb3"         "Smg8"          "Mat2b"         "Sesn3"         "Il15ra"        "Ly6i"          "Rpl17-ps8"    
## [217] "Ifi206"        "Themis2"       "Tmem86a"       "Parp10"        "Gpatch11"      "Zbtb32"        "Oas1a"         "Zfp422"       
## [225] "Serpini1"      "Dhx58"         "Cxcr3"         "BC147527"      "Ifi211"        "Ndufc1"        "Hck"           "Gm16701"      
## [233] "Slc11a1"       "Prkaa2"        "Lsm8"          "Aopep"         "Snx14"         "Inpp4a"        "Hpgds"         "Calhm6"       
## [241] "Zfp846"        "Rnf213"        "Trim21"        "Gpm6a"         "Slc35e2"       "Ssbp2"         "Abtb2"         "Ssbp1"        
## [249] "Marchf1"       "Mtf1"          "Rps18-ps6"     "H2-T24"        "Bpnt1"         "Nt5e"          "Polr1e"        "Coq10b"       
## [257] "Cog1"          "Rangrf"        "Htatip2"       "Dnase1l3"      "Slc22a5"       "Trmt1l"        "Slc23a2"       "H2-DMb1"      
## [265] "Parp3"         "Mettl17"       "Lyrm1"         "Igkc"          "Rhbdf2"        "Slc30a4"       "9930104L06Rik" "Ccdc146"      
## [273] "Gosr1"         "Tmem106a"      "Abhd15"        "Cpne8"         "Gm24265"       "Erlin1"        "Endod1"        "Vps51"        
## [281] "Nfkb1"         "Aicda"         "Slc26a6"
setdiff(Deg3_pos, c(Deg1_pos, Deg2_pos))
##  [1] "Rnaseh2a" "Dmrta2"   "Tstd3"    "Fastkd2"  "Rad52"    "Cox6a2"   "Dpf1"     "Tnnt2"    "Abcb4"    "Cd28"     "Pmel"     "Ltk"     
## [13] "Zfp113"   "Poli"     "H2bc3"    "Mapk12"
Deg12_intersect_neg <- intersect(Deg1_neg, Deg2_neg)
Deg123_intersect_neg <- intersect(Deg12_intersect_neg, Deg3_neg)
Deg23_intersect_neg <- intersect(Deg2_neg, Deg3_neg)
Deg31_intersect_neg <- intersect(Deg3_neg, Deg1_neg)


Deg12_intersect_neg_scecif <- setdiff(Deg12_intersect_neg, Deg123_intersect_neg)
Deg23_intersect_neg_specif <- setdiff(Deg23_intersect_neg, Deg123_intersect_neg)
Deg31_intersect_neg_specif <- setdiff(Deg31_intersect_neg, Deg123_intersect_neg)

setdiff(Deg1_neg, c(Deg2_neg, Deg3_neg))
##  [1] "Phip"          "Ighg2b"        "Zfp709"        "Tlk2"          "H2ac20"        "Lbhd1"         "Pogk"          "Gm20186"      
##  [9] "Rgs10"         "1300002E11Rik" "R3hdm2"        "Gng4"          "Rabgap1l"      "Aicda"         "Smcr8"         "Tcf7l1"       
## [17] "Ggps1"         "Stag2"         "2700038G22Rik" "Marcks"        "Smpd3"         "Gas7"          "4930523C07Rik" "Samd8"        
## [25] "Ago1"          "Ik"            "Rpph1"
setdiff(Deg2_neg, c(Deg1_neg, Deg3_neg))
##   [1] "Cdh1"          "Ckap4"         "St3gal5"       "Zcchc24"       "Txnrd3"        "Neurl1b"       "Pygm"          "Dipk1b"       
##   [9] "Cd22"          "Grb7"          "Crhbp"         "Prkcb"         "Mki67"         "Lta"           "Strbp"         "Cd248"        
##  [17] "Actn1"         "Klhdc2"        "Trp53i11"      "Ramp1"         "Itih5"         "Nsg2"          "Mgll"          "Mier3"        
##  [25] "Ndrg1"         "Adgrl1"        "Tac4"          "Nuak1"         "Tyro3"         "Lat"           "Scube3"        "Ptgs1"        
##  [33] "Nedd9"         "Coro7"         "Otub2"         "Kifc5b"        "Ptp4a3"        "Dgkd"          "Tox"           "Gm36120"      
##  [41] "Vangl2"        "Cd37"          "Endou"         "Atad5"         "Plcl1"         "Epb41l5"       "Hs3st2"        "Slc16a3"      
##  [49] "1700019D03Rik" "Ehd2"          "Rgs7bp"        "Def6"          "Pycr1"         "Ets1"          "Satb1"         "Jag2"         
##  [57] "Napg"          "Ctnnbip1"      "Irf4"          "Faap100"       "Loxl2"         "Nfia"          "Tcn2"          "Shroom3"      
##  [65] "Gas2l3"        "Scd1"          "Bok"           "Kdm7a"         "Gm48768"       "Irs2"          "Pcx"           "Cmah"         
##  [73] "Abi2"          "Adgre5"        "Gmpr"          "Iqsec1"        "Pola1"         "Hbb-bt"        "Card11"        "Dusp7"        
##  [81] "Prdm8"         "E2f7"          "Pou2f1"        "Hvcn1"         "Icam2"         "1700120C14Rik" "Ano10"         "Igf2bp2"      
##  [89] "Mfsd4a"        "Pecr"          "Cit"           "Emp1"          "Egfl7"         "Klrb1c"        "Tmed6"         "Smtnl2"       
##  [97] "Pik3c2a"       "Atp2a3"        "Bhlhe40"       "Sema4b"        "Nab2"          "Tcf7l2"        "Jcad"          "Tbx19"        
## [105] "Slc37a2"       "Sema5a"        "Brdt"          "C730034F03Rik" "Nkd2"          "C2cd5"         "Abca2"         "Arhgef1"      
## [113] "Gm9227"        "S100a11"       "Slc47a1"       "Kank2"         "Ncs1"          "BC049352"      "Nek1"          "Fam160b1"     
## [121] "Abraxas1"      "Pla2g2f"       "Gm42047"       "Gdf11"         "Batf"          "Ccdc47"        "Crisp1"        "Pygl"         
## [129] "Ltb"           "0610040J01Rik" "A730063M14Rik" "Stradb"        "Cryl1"         "Rtn1"          "Ephx1"         "Cdc42ep4"     
## [137] "Accs"          "Rbms3"         "Slc44a1"       "4632427E13Rik" "Rcan3"         "St6gal1"       "Ccpg1"         "Rapgefl1"     
## [145] "Tnfrsf9"       "Glis2"         "Slc17a8"       "Bfsp2"         "Tmem176b"      "Fam8a1"        "E130308A19Rik" "Tmem158"      
## [153] "Mcm8"          "Rapgef3"       "Tsc22d3"       "Rasl10a"       "Chst15"        "Syndig1l"      "Map3k1"        "Srgap3"       
## [161] "Stox2"         "H2ac6"         "Vldlr"         "Usp6nl"        "Smpd1"         "Slc25a40"      "Kdm4c"         "Abcd3"        
## [169] "Mbd4"          "Tnni2"         "Plin3"         "Fhl1"          "Azin1"         "Prxl2c"        "Pcdh11x"       "Hes1"         
## [177] "Kif21b"        "Cth"           "Mybl1"         "Armt1"         "Izumo1r"       "Cdhr2"         "Mtss1"         "Cbx7"         
## [185] "Ccdc112"       "Zfp821"        "Cdhr4"         "Smyd4"         "Gm9993"        "Cradd"         "Uaca"          "Gm36684"      
## [193] "Gpt2"          "Hook3"         "Acp5"          "Wdr91"         "Arhgef17"      "Gm11824"       "Gfi1b"         "Hid1"         
## [201] "Gm26531"       "Bclaf3"        "Pcdh9"         "Ier3"          "Sel1l3"        "Abtb1"         "Gkap1"         "Rtkn2"        
## [209] "Sorcs2"        "Qrfp"          "Tasor2"        "Apbb2"         "Syngr1"        "Rprm"          "4933427D14Rik" "Ddx25"        
## [217] "Zfp318"        "Dalrd3"        "Prpf40b"       "Mpp3"          "Ribc1"         "Itga9"         "Zyg11b"        "Cavin1"       
## [225] "Sult1d1"       "Abcb4"         "Gm867"         "Angpt1"        "Grip1"         "Acy3"          "Mpp7"          "a"            
## [233] "Vps13b"        "Cpm"           "Txnrd2"        "Hs3st1"        "Ltbp4"         "Tulp4"         "Chd3"          "Pcbd1"        
## [241] "Nme3"          "Spats2"        "Dusp23"        "Slc7a3"        "Tctn1"         "Zfp820"        "Phldb1"        "Zfp956"       
## [249] "Dync2h1"       "Cdkal1"        "Six5"          "Ccdc88c"       "Nup160"        "Meis2"         "Zan"           "Gas2"         
## [257] "Ramp2"
setdiff(Deg3_neg, c(Deg1_neg, Deg2_neg))
##  [1] "Utp20"     "Snrpd3"    "Dleu2"     "Rbm19"     "Slfn2"     "Glb1l"     "Lmo7"      "Cep290"    "Maged1"    "Tbk1"      "Svip"     
## [12] "Rn7sk"     "Kmt2d"     "Iqce"      "Gm47304"   "Bmp2k"     "Clock"     "Eif4enif1" "Rmnd1"     "Enc1"      "Ap1ar"     "Sema7a"   
## [23] "Mir17hg"   "Relch"
cid <- 0
###### volcano plots for the publication ######

sessionInfo

sessionInfo()

# saving session info
sink(file.path(results_dir, paste0("sessionInfo_", project_name,".txt")))
sessionInfo()
sink()

renv::snapshot(lockfile = file.path(base_dir, paste0(project_name, "_renv.lock")))
renv::status(lockfile = file.path(base_dir, paste0(project_name, "_renv.lock")))
---
title: "NK cells immunoediting"
author: "Aleksandr Bykov + code from Peter Repiscak"
date: '`r format(Sys.time(), "%d %B %Y")`'
output:
 html_document:
  theme: readable
  highlight: tango
  code_folding: hide
  code_download: true
#  toc: true
#  toc_depth: 2
#  toc_float: true
always_allow_html: true
params:
  config_file: "nk_tum_immunoediting_config.yaml"
---

# Project description 

Uncovering immune evasion mechanisms of leukemic cells from natural killer cells. In this sub-project RNA sequencing of leukemic cells is used to discover transcriptional changes resulting in NK cell-resistance. There is another sub-project using ATAC-seq (transposase-accessible chromatin) to epigenetic changes resulting in NK cell-resistance - this is not part of this report.

## Experimental design

Taken from discussion summary ('21009_Porject discussion summary.docx'): 

*	4 different cell lines (p185#13G, p185#13H, p185#15M, p185#15O). ADD description about cell line differences.
*	All conditions in technical triplicates and 2 biological replicates (biological replicates can be added any time, if there is a need).
* Technical replicate means that we already start the co-culture in a separate well.

![Biological and technical replicates](NKproject_bio_technical_replicates.png)

## Timeline of the experiment

*	On time point Day 4 we FACS sort the original tumor cells (like Day 0) and the tumor cells which were co-cultured for 4 days with NK cells for the following analyses: ATAC seq, RNA seq and barcode analysis.
Additionally we co-culture a certain amount of the same tumor cells again with NK cells for the analysis of Day 10.
*	On time point Day 10 we FACS sort the tumor cells which were co-cultured for 10 days with NK cells for the same analyses: ATAC seq, RNA seq and barcode analysis.

![Experiment Timepoints](NKproject_experiment_timepoints.png)

# RNA-seq data analysis {.tabset .tabset-pills}

RNA-seq methods: QuantSeq 3' mRNA-Seq - 1x50bp HiSeq 3000/4000. 

Pre-processing: ADD methods+versions for preprocessing
![Pre-processing pipeline](NKproject_preprocessing_pipeline.png)

Downstream analysis: ADD methods+versions for downstream!


[MultiQC report](NKproject_overall_multiqc.html)
ADD full multiqc report description. Few observations:

* 3' bias (expected for Quant-seq)
* EXP9.8 has longer reads (?) 120bp as opposed to 51bp for every other experiment. This is also reflected in a higher alignment to coding region. Further, a clear separation of EXP9.8 from other experiments is observed on the PCA plot. However, also EXP9.7 is somehow separated (along the PC2) from the other experiments. The quantification and alignment were done with the same settings all the experiments


```{r analysis_parameters, include=FALSE}
# Specifying parameters here ----
# remotes::install_github("rstudio/renv@v0.16.0")
if(!("renv" %in% installed.packages())){install.packages("renv", version="0.16.0")}  # docker has 0.15.4; change in production image! 
if(!("import" %in% installed.packages())){renv::install("import")}
if(!("yaml" %in% installed.packages())){renv::install("yaml")}
if(!("knitr" %in% installed.packages())){renv::install("knitr")}

# setting up initial parameters
base_dir <- "/home/rstudio/workspace/"
data_dir <- file.path(base_dir, "datasets") 
dir.create(data_dir)
annotation_dir = paste0(data_dir, "/annotations/")
dir.create(annotation_dir)
results_dir <- file.path(base_dir, "results_dir") 
dir.create(results_dir)
setwd(base_dir)

# project setup
# loading config file ----
config_file <- file.path(base_dir, "nk_tum_immunoediting_config.yaml")  
config <- yaml::read_yaml(config_file)

#project setup
project_name <- config$project_name
nthreads <- config$nthreads # e.g. 4 for furrr multisession

# this make take a while...
# https://rstudio.github.io/renv/reference/config.html
# getOption(x, default = NULL)
# renv::settings$use.cache()
# getOption('renv.config.pak.enabled')
# if the project does not automatically activate run:
if(Sys.getenv("RENV_PATHS_CACHE") != "/renv_cache") {Sys.setenv(RENV_PATHS_CACHE = "/renv_cache")}
if(Sys.getenv("RENV_PATHS_LIBRARY") != "/home/rstudio/renv_library") {Sys.setenv(RENV_PATHS_LIBRARY = "/home/rstudio/renv_library")}
#if(Sys.getenv("RENV_CONFIG_PAK_ENABLED") != "TRUE") {Sys.setenv(RENV_CONFIG_PAK_ENABLED = TRUE)} # add pak, targets and benchmarkme to docker!!!
# setting root dir
# 0. renv::activate
renv::activate(project = base_dir)

#IF packages are not loaded properly, use: 
# renv::restore()

# Only for manual installation of the project and recovery from the lock file.
# renv::init(project = base_dir, bare=TRUE, bioconductor = "3.16")
# FOR the first run restore environment and packages from renv.lock 
# 1. restore original environment
# renv::restore(project = base_dir, lockfile = file.path(base_dir, paste0(project_name, "_renv.lock")), prompt = FALSE)
# file.copy(from = file.path(base_dir, paste0(project_name, "_renv.lock")), to = file.path(base_dir, "renv.lock")) # to use with renv::diagnostics()

# for some reason apeglm package cannot be spanshotted to the renv.lock file. so, it must be installed manually.
# BiocManager::install("apeglm")

```

```{r initial_setup, include=FALSE}
# move to an internal hidden chunk?

set.seed(42)

import::from(knitr, opts_chunk)

# report options ----
# knitr::opts_chunk
opts_chunk$set(echo = TRUE,
                      eval = TRUE,
                      cache.lazy = FALSE,
                      message=FALSE,
                      warning=FALSE,
                      dev = "png")

# cache.path = file.path(results_dir,"report","cache/")
# fig.path = file.path(results_dir,"report","files/")                     
                     
options(width=100)

```


```{r run in the terminal, eval = FALSE, include = FALSE}
# run in terminal to generate report
rmarkdown::render(output_file = file.path(base_dir, "nk_tum_immunoediting.html"), 
                  input = here::here("nk_tum_immunoediting.Rmd"))
```

```{r loading libraries, message=FALSE, eval=TRUE, include=TRUE}
# importing only key functions that are actually used - not to polute namespace!
import::from(readr, read_csv)
import::from(magrittr, "%>%")
import::from(dplyr, mutate, select, filter, rename, arrange, desc, group_by, summarise, ungroup)  # dplyr_mutate = mutate
import::from(purrr, map)
import::from(future, plan, multisession, sequential)
import::from(furrr, furrr_options, future_map2)
import::from(ggplot2, .all=TRUE) # importing all as there is too many
import::from(grid, gpar) # needed in complexheatmap
import::from(kableExtra, kable_styling, kbl)
import::from(.from = SummarizedExperiment, colData, assay) # used in every .Rmd
import::from(.from = tableone, CreateTableOne)

```

## Preparing datasets 

```{r, child=here::here("00_prepare_datasets.Rmd"), include=TRUE, eval = TRUE}
```

## Exploratory Data Analysis - metadata

```{r, child=here::here("01_eda_metadata.Rmd"), include=TRUE, eval = TRUE}
```

## Exploratory Data Analysis - expression

```{r, child=here::here("02_eda_expression.Rmd"), include=TRUE, eval = TRUE}
```

## Results - TpNK_tp2_vs_Tonly_tp1 

```{r, child=here::here("03_results_TpNK_tp2_vs_Tonly_tp1_Aleks_AB_CD_separated.Rmd"), include=TRUE, eval = TRUE}
```

## Results - EXP9.8 comparing WT vs KO, IFNG and PrfKO_NK timepoints

```{r, child=here::here("04_exp9.8_KO_WT_stim_comparison.Rmd"), include=TRUE, eval = TRUE}
```

# sessionInfo
```{r include=TRUE, eval=FALSE, message=FALSE, warning=FALSE}
sessionInfo()

# saving session info
sink(file.path(results_dir, paste0("sessionInfo_", project_name,".txt")))
sessionInfo()
sink()

renv::snapshot(lockfile = file.path(base_dir, paste0(project_name, "_renv.lock")))
renv::status(lockfile = file.path(base_dir, paste0(project_name, "_renv.lock")))

```
